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2004


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Minimum Sum-Squared Residue based clustering of Gene Expression Data

Cho, H., Guan, Y., Dhillon, I., Sra, S.

In SIAM Data Mining, pages: 00-00, SDM, April 2004 (inproceedings)

GZIP [BibTex]

2004

GZIP [BibTex]


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Preservation of Neighborhood Relation under Input to Feature Space Mapping in SVM Training

Shin, H., Cho, S.

In Proc. of the 33rd International Conference on Computers and Industrial Engineering (C&IE 2004), pages: 1-10, the 33rd International Conference on Computers and Industrial Engineering (C&IE), April 2004, in CD (inproceedings)

[BibTex]

[BibTex]


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Unifying Colloborative and Content-Based Filtering.

Basilico, J., Hofmann, T.

In ACM International Conference Proceeding Series, pages: 65 , (Editors: Greiner, R. , D. Schuurmans), ACM Press, New York, USA, ICLM, 2004 (inproceedings)

Abstract
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that allows simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.

PDF [BibTex]

PDF [BibTex]


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Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models

Dubey, A., Hwang, S., Rangel, C., Rasmussen, CE., Ghahramani, Z., Wild, DL.

In Pacific Symposium on Biocomputing 2004; Vol. 9, pages: 399-410, World Scientific Publishing, Singapore, Pacific Symposium on Biocomputing, 2004 (inproceedings)

Abstract
We describe a novel approach to the problem of automatically clustering protein sequences and discovering protein families, subfamilies etc., based on the thoery of infinite Gaussian mixture models. This method allows the data itself to dictate how many mixture components are required to model it, and provides a measure of the probability that two proteins belong to the same cluster. We illustrate our methods with application to three data sets: globin sequences, globin sequences with known tree-dimensional structures and G-pretein coupled receptor sequences. The consistency of the clusters indicate that that our methods is producing biologically meaningful results, which provide a very good indication of the underlying families and subfamilies. With the inclusion of secondary structure and residue solvent accessibility information, we obtain a classification of sequences of known structure which reflects and extends their SCOP classifications. A supplementary web site containing larger versions of the figures is available at http://public.kgi.edu/~wild/PSB04

PDF [BibTex]

PDF [BibTex]


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Efficient Approximations for Support Vector Machines in Object Detection

Kienzle, W., BakIr, G., Franz, M., Schölkopf, B.

In DAGM 2004, pages: 54-61, (Editors: CE Rasmussen and HH Bülthoff and B Schölkopf and MA Giese), Springer, Berlin, Germany, Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

Abstract
We present a new approximation scheme for support vector decision functions in object detection. In the present approach we are building on an existing algorithm where the set of support vectors is replaced by a smaller so-called reduced set of synthetic points. Instead of finding the reduced set via unconstrained optimization, we impose a structural constraint on the synthetic vectors such that the resulting approximation can be evaluated via separable filters. Applications that require scanning an entire image can benefit from this representation: when using separable filters, the average computational complexity for evaluating a reduced set vector on a test patch of size (h x w) drops from O(hw) to O(h+w). We show experimental results on handwritten digits and face detection.

PDF [BibTex]

PDF [BibTex]


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Kernel Methods for Manifold Estimation

Schölkopf, B.

In Proceedings in Computational Statistics, pages: 441-452, (Editors: J Antoch), Physica-Verlag/Springer, Heidelberg, Germany, COMPSTAT, 2004 (inproceedings)

[BibTex]

[BibTex]


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A Regularization Framework for Learningfrom Graph Data

Zhou, D., Schölkopf, B.

In ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields, pages: 132-137, ICML, 2004 (inproceedings)

Abstract
The data in many real-world problems can be thought of as a graph, such as the web, co-author networks, and biological networks. We propose a general regularization framework on graphs, which is applicable to the classification, ranking, and link prediction problems. We also show that the method can be explained as lazy random walks. We evaluate the method on a number of experiments.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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A kernel view of the dimensionality reduction of manifolds

Ham, J., Lee, D., Mika, S., Schölkopf, B.

In Proceedings of the Twenty-First International Conference on Machine Learning, pages: 369-376, (Editors: CE Brodley), ACM, New York, NY, USA, ICML, 2004, also appeared as MPI-TR 110 (inproceedings)

Abstract
We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local neighborhood information to construct a global embedding of the manifold. We show how all three algorithms can be described as kernel PCA on specially constructed Gram matrices, and illustrate the similarities and differences between the algorithms with representative examples.

PDF [BibTex]

PDF [BibTex]


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Protein Functional Class Prediction with a Combined Graph

Shin, H., Tsuda, K., Schölkopf, B.

In Proceedings of the Korean Data Mining Conference, pages: 200-219, Proceedings of the Korean Data Mining Conference, 2004 (inproceedings)

Abstract
In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as protein-protein interactions, genetic interactions, or co-participation in a protein complex, etc. Relying on similarities between nodes, each graph can be used independently for prediction of protein function. However, since different graphs contain partly independent and partly complementary information about the problem at hand, one can enhance the total information extracted by combining all graphs. In this paper, we propose a method for integrating multiple graphs within a framework of semi-supervised learning. The method alternates between minimizing the objective function with respect to network output and with respect to combining weights. We apply the method to the task of protein functional class prediction in yeast. The proposed method performs significantly better than the same algorithm trained on any single graph.

PDF [BibTex]

PDF [BibTex]


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Learning from Labeled and Unlabeled Data Using Random Walks

Zhou, D., Schölkopf, B.

In Pattern Recognition, Proceedings of the 26th DAGM Symposium, pages: 237-244, (Editors: Rasmussen, C.E., H.H. Bülthoff, M.A. Giese and B. Schölkopf), Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

Abstract
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels of the unlabeled points. Any supervised learning algorithm can be applied to this problem, for instance, Support Vector Machines (SVMs). The problem of our interest is if we can implement a classifier which uses the unlabeled data information in some way and has higher accuracy than the classifiers which use the labeled data only. Recently we proposed a simple algorithm, which can substantially benefit from large amounts of unlabeled data and demonstrates clear superiority to supervised learning methods. In this paper we further investigate the algorithm using random walks and spectral graph theory, which shed light on the key steps in this algorithm.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Multivariate Regression via Stiefel Manifold Constraints

BakIr, G., Gretton, A., Franz, M., Schölkopf, B.

In Lecture Notes in Computer Science, Vol. 3175, pages: 262-269, (Editors: CE Rasmussen and HH Bülthoff and B Schölkopf and MA Giese), Springer, Berlin, Germany, Pattern Recognition, Proceedings of the 26th DAGM Symposium, 2004 (inproceedings)

Abstract
We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered independently. By contrast, in our approach the feature construction and the regression estimation are performed jointly, directly minimizing a loss function that we specify, subject to a rank constraint. A major advantage of this approach is that the loss is no longer chosen according to the algorithmic requirements, but can be tailored to the characteristics of the task at hand; the features will then be optimal with respect to this objective, and dependence between the outputs can be exploited.

PostScript [BibTex]

PostScript [BibTex]


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Implicit estimation of Wiener series

Franz, M., Schölkopf, B.

In Machine Learning for Signal Processing XIV, Proc. 2004 IEEE Signal Processing Society Workshop, pages: 735-744, (Editors: A Barros and J Principe and J Larsen and T Adali and S Douglas), IEEE, New York, Machine Learning for Signal Processing XIV, Proc. IEEE Signal Processing Society Workshop, 2004 (inproceedings)

Abstract
The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear systems. We propose an implicit estimation method based on regression in a reproducing kernel Hilbert space that alleviates these problems. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Hilbertian Metrics on Probability Measures and their Application in SVM’s

Hein, H., Lal, T., Bousquet, O.

In Pattern Recognition, Proceedings of th 26th DAGM Symposium, 3175, pages: 270-277, Lecture Notes in Computer Science, (Editors: Rasmussen, C. E., H. H. Bülthoff, M. Giese and B. Schölkopf), Pattern Recognition, Proceedings of th 26th DAGM Symposium, 2004 (inproceedings)

Abstract
The goal of this article is to investigate the field of Hilbertian metrics on probability measures. Since they are very versatile and can therefore be applied in various problems they are of great interest in kernel methods. Quit recently Tops{o}e and Fuglede introduced a family of Hilbertian metrics on probability measures. We give basic properties of the Hilbertian metrics of this family and other used metrics in the literature. Then we propose an extension of the considered metrics which incorporates structural information of the probability space into the Hilbertian metric. Finally we compare all proposed metrics in an image and text classification problem using histogram data.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Gasussian process model based predictive control

Kocijan, J., Murray-Smith, R., Rasmussen, CE., Girard, A.

In Proceedings of the ACC 2004, pages: 2214-2219, Proceedings of the ACC, 2004 (inproceedings)

Abstract
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identi cation of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coef cients to be optimised. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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A New Variational Framework for Rigid-Body Alignment

Kato, T., Tsuda, K., Tomii, K., Asai, K.

In Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004), pages: 171-179, (Editors: Fred, A.,T. Caelli, R.P.W. Duin, A. Campilho and D. de Ridder), Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR) and Statistical Pattern Recognition (SPR), 2004 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Practical Method for Blind Inversion of Wiener Systems

Zhang, K., Chan, L.

In Proceedings of International Joint Conference on Neural Networks (IJCNN 2004), pages: 2163-2168, International Joint Conference on Neural Networks (IJCNN), 2004, Volume 3 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Semi-supervised kernel regression using whitened function classes

Franz, M., Kwon, Y., Rasmussen, C., Schölkopf, B.

In Pattern Recognition, Proceedings of the 26th DAGM Symposium, Lecture Notes in Computer Science, Vol. 3175, LNCS 3175, pages: 18-26, (Editors: CE Rasmussen and HH Bülthoff and MA Giese and B Schölkopf), Springer, Berlin, Gerrmany, 26th DAGM Symposium, 2004 (inproceedings)

Abstract
The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique that results in an implicit whitening of the basis functions by penalizing directions in function space with a large prior variance. The regularization term is computed from unlabelled input data that characterizes the input distribution. Tests on two datasets using polynomial basis functions showed an improved average performance compared to standard ridge regression.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O.

In Learning Theory and Kernel Machines, pages: 72-86, (Editors: Schölkopf, B. and Warmuth, M. K.), Springer, Heidelberg, Germany, 16. Annual Conference on Computational Learning Theory / COLT Kernel, 2004 (inproceedings)

Abstract
In this article we construct a maximal margin classification algorithm for arbitrary metric spaces. At first we show that the Support Vector Machine (SVM) is a maximal margin algorithm for the class of metric spaces where the negative squared distance is conditionally positive definite (CPD). This means that the metric space can be isometrically embedded into a Hilbert space, where one performs linear maximal margin separation. We will show that the solution only depends on the metric, but not on the kernel. Following the framework we develop for the SVM, we construct an algorithm for maximal margin classification in arbitrary metric spaces. The main difference compared with SVM is that we no longer embed isometrically into a Hilbert space, but a Banach space. We further give an estimate of the capacity of the function class involved in this algorithm via Rademacher averages. We recover an algorithm of Graepel et al. [6].

PDF PostScript PDF DOI [BibTex]

PDF PostScript PDF DOI [BibTex]


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On the Convergence of Spectral Clustering on Random Samples: The Normalized Case

von Luxburg, U., Bousquet, O., Belkin, M.

In Proceedings of the 17th Annual Conference on Learning Theory, pages: 457-471, Proceedings of the 17th Annual Conference on Learning Theory, 2004 (inproceedings)

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Learning Movement Primitives

Schaal, S., Peters, J., Nakanishi, J., Ijspeert, A.

In 11th International Symposium on Robotics Research (ISRR2003), pages: 561-572, (Editors: Dario, P. and Chatila, R.), Springer, ISRR, 2004, clmc (inproceedings)

Abstract
This paper discusses a comprehensive framework for modular motor control based on a recently developed theory of dynamic movement primitives (DMP). DMPs are a formulation of movement primitives with autonomous nonlinear differential equations, whose time evolution creates smooth kinematic control policies. Model-based control theory is used to convert the outputs of these policies into motor commands. By means of coupling terms, on-line modifications can be incorporated into the time evolution of the differential equations, thus providing a rather flexible and reactive framework for motor planning and execution. The linear parameterization of DMPs lends itself naturally to supervised learning from demonstration. Moreover, the temporal, scale, and translation invariance of the differential equations with respect to these parameters provides a useful means for movement recognition. A novel reinforcement learning technique based on natural stochastic policy gradients allows a general approach of improving DMPs by trial and error learning with respect to almost arbitrary optimization criteria. We demonstrate the different ingredients of the DMP approach in various examples, involving skill learning from demonstration on the humanoid robot DB, and learning biped walking from demonstration in simulation, including self-improvement of the movement patterns towards energy efficiency through resonance tuning.

link (url) DOI [BibTex]

link (url) DOI [BibTex]

2003


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How to Deal with Large Dataset, Class Imbalance and Binary Output in SVM based Response Model

Shin, H., Cho, S.

In Proc. of the Korean Data Mining Conference, pages: 93-107, Korean Data Mining Conference, December 2003, Best Paper Award (inproceedings)

Abstract
[Abstract]: Various machine learning methods have made a rapid transition to response modeling in search of improved performance. And support vector machine (SVM) has also been attracting much attention lately. This paper presents an SVM response model. We are specifically focusing on the how-to’s to circumvent practical obstacles, such as how to face with class imbalance problem, how to produce the scores from an SVM classifier for lift chart analysis, and how to evaluate the models on accuracy and profit. Besides coping with the intractability problem of SVM training caused by large marketing dataset, a previously proposed pattern selection algorithm is introduced. SVM training accompanies time complexity of the cube of training set size. The pattern selection algorithm picks up important training patterns before SVM response modeling. We made comparison on SVM training results between the pattern selection algorithm and random sampling. Three aspects of SVM response models were evaluated: accuracies, lift chart analysis, and computational efficiency. The SVM trained with selected patterns showed a high accuracy, a high uplift in profit and in response rate, and a high computational efficiency.

PDF [BibTex]

2003

PDF [BibTex]


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On the Complexity of Learning the Kernel Matrix

Bousquet, O., Herrmann, D.

In Advances in Neural Information Processing Systems 15, pages: 399-406, (Editors: Becker, S. , S. Thrun, K. Obermayer), The MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We investigate data based procedures for selecting the kernel when learning with Support Vector Machines. We provide generalization error bounds by estimating the Rademacher complexities of the corresponding function classes. In particular we obtain a complexity bound for function classes induced by kernels with given eigenvectors, i.e., we allow to vary the spectrum and keep the eigenvectors fix. This bound is only a logarithmic factor bigger than the complexity of the function class induced by a single kernel. However, optimizing the margin over such classes leads to overfitting. We thus propose a suitable way of constraining the class. We use an efficient algorithm to solve the resulting optimization problem, present preliminary experimental results, and compare them to an alignment-based approach.

PDF Web [BibTex]

PDF Web [BibTex]


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Bayesian Monte Carlo

Rasmussen, CE., Ghahramani, Z.

In Advances in Neural Information Processing Systems 15, pages: 489-496, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the incorporation of prior knowledge, such as smoothness of the integrand, into the estimation. In a simple problem we show that this outperforms any classical importance sampling method. We also attempt more challenging multidimensional integrals involved in computing marginal likelihoods of statistical models (a.k.a. partition functions and model evidences). We find that Bayesian Monte Carlo outperformed Annealed Importance Sampling, although for very high dimensional problems or problems with massive multimodality BMC may be less adequate. One advantage of the Bayesian approach to Monte Carlo is that samples can be drawn from any distribution. This allows for the possibility of active design of sample points so as to maximise information gain.

PDF Web [BibTex]

PDF Web [BibTex]


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Discriminative Learning for Label Sequences via Boosting

Altun, Y., Hofmann, T., Johnson, M.

In Advances in Neural Information Processing Systems 15, pages: 977-984, (Editors: Becker, S. , S. Thrun, K. Obermayer ), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
This paper investigates a boosting approach to discriminative learning of label sequences based on a sequence rank loss function.

PDF Web [BibTex]

PDF Web [BibTex]


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Control, Planning, Learning, and Imitation with Dynamic Movement Primitives

Schaal, S., Peters, J., Nakanishi, J., Ijspeert, A.

In IROS 2003, pages: 1-21, Workshop on Bilateral Paradigms on Humans and Humanoids, IEEE International Conference on Intelligent Robots and Systems, October 2003 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Cluster Kernels for Semi-Supervised Learning

Chapelle, O., Weston, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 15, pages: 585-592, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach.

PDF Web [BibTex]

PDF Web [BibTex]


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Mismatch String Kernels for SVM Protein Classification

Leslie, C., Eskin, E., Weston, J., Noble, W.

In Advances in Neural Information Processing Systems 15, pages: 1417-1424, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure sequence similarity based on shared occurrences of k-length subsequences, counted with up to m mismatches, and do not rely on any generative model for the positive training sequences. We compute the kernels efficiently using a mismatch tree data structure and report experiments on a benchmark SCOP dataset, where we show that the mismatch kernel used with an SVM classifier performs as well as the Fisher kernel, the most successful method for remote homology detection, while achieving considerable computational savings.

PDF Web [BibTex]

PDF Web [BibTex]


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Multiple-step ahead prediction for non linear dynamic systems: A Gaussian Process treatment with propagation of the uncertainty

Girard, A., Rasmussen, CE., Quiñonero-Candela, J., Murray-Smith, R.

In Advances in Neural Information Processing Systems 15, pages: 529-536, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form y_t = f(y_{t-1},...,y_{t-L}), the prediction of y at time t + k is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction.

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Dependency Estimation

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V.

In Advances in Neural Information Processing Systems 15, pages: 873-880, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Incremental Gaussian Processes

Quinonero Candela, J., Winther, O.

In Advances in Neural Information Processing Systems 15, pages: 1001-1008, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
In this paper, we consider Tipping‘s relevance vector machine (RVM) and formalize an incremental training strategy as a variant of the expectation-maximization (EM) algorithm that we call subspace EM. Working with a subset of active basis functions, the sparsity of the RVM solution will ensure that the number of basis functions and thereby the computational complexity is kept low. We also introduce a mean field approach to the intractable classification model that is expected to give a very good approximation to exact Bayesian inference and contains the Laplace approximation as a special case. We test the algorithms on two large data sets with O(10^3-10^4) examples. The results indicate that Bayesian learning of large data sets, e.g. the MNIST database is realistic.

PDF Web [BibTex]

PDF Web [BibTex]


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Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model

Franz, MO., Chahl, JS.

In Advances in Neural Information Processing Systems 15, pages: 1319-1326, (Editors: Becker, S., S. Thrun and K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.

PDF Web [BibTex]

PDF Web [BibTex]


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Clustering with the Fisher score

Tsuda, K., Kawanabe, M., Müller, K.

In Advances in Neural Information Processing Systems 15, pages: 729-736, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
Recently the Fisher score (or the Fisher kernel) is increasingly used as a feature extractor for classification problems. The Fisher score is a vector of parameter derivatives of loglikelihood of a probabilistic model. This paper gives a theoretical analysis about how class information is preserved in the space of the Fisher score, which turns out that the Fisher score consists of a few important dimensions with class information and many nuisance dimensions. When we perform clustering with the Fisher score, K-Means type methods are obviously inappropriate because they make use of all dimensions. So we will develop a novel but simple clustering algorithm specialized for the Fisher score, which can exploit important dimensions. This algorithm is successfully tested in experiments with artificial data and real data (amino acid sequences).

PDF Web [BibTex]

PDF Web [BibTex]


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Derivative observations in Gaussian Process models of dynamic systems

Solak, E., Murray-Smith, R., Leithead, WE., Leith, D., Rasmussen, CE.

In Advances in Neural Information Processing Systems 15, pages: 1033-1040, (Editors: Becker, S., S. Thrun and K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1) It allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference process. This derivative information can be in the form of priors specified by an expert or identified from perturbation data close to equilibrium. 2) It allows a seamless fusion of multiple local linear models in a consistent manner, inferring consistent models and ensuring that integrability constraints are met. 3) It improves dramatically the computational efficiency of Gaussian process models for dynamic system identification, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training set size - traditionally a problem for Gaussian process models.

PDF Web [BibTex]

PDF Web [BibTex]


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Large Margin Methods for Label Sequence Learning

Altun, Y., Hofmann, T.

In pages: 993-996, International Speech Communication Association, Bonn, Germany, 8th European Conference on Speech Communication and Technology (EuroSpeech), September 2003 (inproceedings)

Web [BibTex]

Web [BibTex]


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Fast Pattern Selection Algorithm for Support Vector Classifiers: "Time Complexity Analysis"

Shin, H., Cho, S.

In Lecture Notes in Computer Science (LNCS 2690), LNCS 2690, pages: 1008-1015, Springer-Verlag, Heidelberg, The 4th International Conference on Intelligent Data Engineering (IDEAL), September 2003 (inproceedings)

Abstract
Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. The time complexity of the proposed algorithm is much smaller than that of the naive M^2 algorithm

PDF [BibTex]

PDF [BibTex]


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Marginalized Kernels between Labeled Graphs

Kashima, H., Tsuda, K., Inokuchi, A.

In 20th International Conference on Machine Learning, pages: 321-328, (Editors: Faucett, T. and N. Mishra), 20th International Conference on Machine Learning, August 2003 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Sparse Gaussian Processes: inference, subspace identification and model selection

Csato, L., Opper, M.

In Proceedings, pages: 1-6, (Editors: Van der Hof, , Wahlberg), The Netherlands, 13th IFAC Symposium on System Identifiaction, August 2003, electronical version; Index ThA02-2 (inproceedings)

Abstract
Gaussian Process (GP) inference is a probabilistic kernel method where the GP is treated as a latent function. The inference is carried out using the Bayesian online learning and its extension to the more general iterative approach which we call TAP/EP learning. Sparsity is introduced in this context to make the TAP/EP method applicable to large datasets. We address the prohibitive scaling of the number of parameters by defining a subset of the training data that is used as the support the GP, thus the number of required parameters is independent of the training set, similar to the case of ``Support--‘‘ or ``Relevance--Vectors‘‘. An advantage of the full probabilistic treatment is that allows the computation of the marginal data likelihood or evidence, leading to hyper-parameter estimation within the GP inference. An EM algorithm to choose the hyper-parameters is proposed. The TAP/EP learning is the E-step and the M-step then updates the hyper-parameters. Due to the sparse E-step the resulting algorithm does not involve manipulation of large matrices. The presented algorithm is applicable to a wide variety of likelihood functions. We present results of applying the algorithm on classification and nonstandard regression problems for artificial and real datasets.

PDF GZIP [BibTex]

PDF GZIP [BibTex]


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Adaptive, Cautious, Predictive control with Gaussian Process Priors

Murray-Smith, R., Sbarbaro, D., Rasmussen, CE., Girard, A.

In Proceedings of the 13th IFAC Symposium on System Identification, pages: 1195-1200, (Editors: Van den Hof, P., B. Wahlberg and S. Weiland), Proceedings of the 13th IFAC Symposium on System Identification, August 2003 (inproceedings)

Abstract
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example.

PDF [BibTex]

PDF [BibTex]


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Generative Model-based Clustering of Directional Data

Banerjee, A., Dhillon, I., Ghosh, J., Sra, S.

In Proc. ACK SIGKDD, pages: 00-00, KDD, August 2003 (inproceedings)

GZIP [BibTex]

GZIP [BibTex]


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Hidden Markov Support Vector Machines

Altun, Y., Tsochantaridis, I., Hofmann, T.

In pages: 4-11, (Editors: Fawcett, T. , N. Mishra), AAAI Press, Menlo Park, CA, USA, Twentieth International Conference on Machine Learning (ICML), August 2003 (inproceedings)

Web [BibTex]

Web [BibTex]


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How Many Neighbors To Consider in Pattern Pre-selection for Support Vector Classifiers?

Shin, H., Cho, S.

In Proc. of INNS-IEEE International Joint Conference on Neural Networks (IJCNN 2003), pages: 565-570, IJCNN, July 2003 (inproceedings)

Abstract
Training support vector classifiers (SVC) requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVC training, we previously proposed a preprocessing algorithm which selects only the patterns in the overlap region around the decision boundary, based on neighborhood properties [8], [9], [10]. The k-nearest neighbors’ class label entropy for each pattern was used to estimate the pattern’s proximity to the decision boundary. The value of parameter k is critical, yet has been determined by a rather ad-hoc fashion. We propose in this paper a systematic procedure to determine k and show its effectiveness through experiments.

PDF [BibTex]

PDF [BibTex]


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On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics

Ilg, W., Bakir, GH., Mezger, J., Giese, MA.

In Humanoids Proceedings, pages: 0-0, Humanoids Proceedings, July 2003, electronical version (inproceedings)

Abstract
In this paper we present a learning-based approach for the modelling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMS. We derive a hierarchical algorithm that, in a first step, identifies automatically movement elements in movement sequences based on a coarse spatio-temporal description, and in a second step models these movement primitives by approximation through linear combinations of learned example movement trajectories. We describe the different steps of the algorithm and show how it can be applied for modelling and synthesis of complex sequences of human movements that contain movement elements with variable style. The proposed method is demonstrated on different applications of movement representation relevant for imitation learning of movement styles in humanoid robotics.

PDF [BibTex]

PDF [BibTex]


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Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences

Altun, Y., Johnson, M., Hofmann, T.

In pages: 145-152, (Editors: Collins, M. , M. Steedman), ACL, East Stroudsburg, PA, USA, Conference on Empirical Methods in Natural Language Processing (EMNLP) , July 2003 (inproceedings)

Abstract
Discriminative models have been of interest in the NLP community in recent years. Previous research has shown that they are advantageous over generative models. In this paper, we investigate how different objective functions and optimization methods affect the performance of the classifiers in the discriminative learning framework. We focus on the sequence labelling problem, particularly POS tagging and NER tasks. Our experiments show that changing the objective function is not as effective as changing the features included in the model.

Web [BibTex]

Web [BibTex]


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Time Complexity Analysis of Fast Pattern Selection Algorithm for SVM

Shin, H., Cho, S.

In Proc. of the Korean Data Mining Conference, pages: 221-231, Korean Data Mining Conference, June 2003 (inproceedings)

[BibTex]

[BibTex]


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Fast Pattern Selection for Support Vector Classifiers

Shin, H., Cho, S.

In PAKDD 2003, pages: 376-387, (Editors: Whang, K.-Y. , J. Jeon, K. Shim, J. Srivastava), Springer, Berlin, Germany, 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2003 (inproceedings)

Abstract
Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. Preliminary simulation results were promising: Up to two orders of magnitude, training time reduction was achieved including the preprocessing, without any loss in classification accuracies.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Scaling Reinforcement Learning Paradigms for Motor Control

Peters, J., Vijayakumar, S., Schaal, S.

In JSNC 2003, 10, pages: 1-7, 10th Joint Symposium on Neural Computation (JSNC), May 2003 (inproceedings)

Abstract
Reinforcement learning offers a general framework to explain reward related learning in artificial and biological motor control. However, current reinforcement learning methods rarely scale to high dimensional movement systems and mainly operate in discrete, low dimensional domains like game-playing, artificial toy problems, etc. This drawback makes them unsuitable for application to human or bio-mimetic motor control. In this poster, we look at promising approaches that can potentially scale and suggest a novel formulation of the actor-critic algorithm which takes steps towards alleviating the current shortcomings. We argue that methods based on greedy policies are not likely to scale into high-dimensional domains as they are problematic when used with function approximation – a must when dealing with continuous domains. We adopt the path of direct policy gradient based policy improvements since they avoid the problems of unstabilizing dynamics encountered in traditional value iteration based updates. While regular policy gradient methods have demonstrated promising results in the domain of humanoid notor control, we demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. Based on this, it is proved that Kakade’s ‘average natural policy gradient’ is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges with probability one to the nearest local minimum in Riemannian space of the cost function. The algorithm outperforms nonnatural policy gradients by far in a cart-pole balancing evaluation, and offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems. Keywords: Reinforcement learning, neurodynamic programming, actorcritic methods, policy gradient methods, natural policy gradient

PDF Web [BibTex]

PDF Web [BibTex]


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A case based comparison of identification with neural network and Gaussian process models.

Kocijan, J., Banko, B., Likar, B., Girard, A., Murray-Smith, R., Rasmussen, CE.

In Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003, 1, pages: 137-142, (Editors: Ruano, E.A.), Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS, April 2003 (inproceedings)

Abstract
In this paper an alternative approach to black-box identification of non-linear dynamic systems is compared with the more established approach of using artificial neural networks. The Gaussian process prior approach is a representative of non-parametric modelling approaches. It was compared on a pH process modelling case study. The purpose of modelling was to use the model for control design. The comparison revealed that even though Gaussian process models can be effectively used for modelling dynamic systems caution has to be axercised when signals are selected.

PDF [BibTex]

PDF [BibTex]


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On-Line One-Class Support Vector Machines. An Application to Signal Segmentation

Gretton, A., Desobry, ..

In IEEE ICASSP Vol. 2, pages: 709-712, IEEE ICASSP, April 2003 (inproceedings)

Abstract
In this paper, we describe an efficient algorithm to sequentially update a density support estimate obtained using one-class support vector machines. The solution provided is an exact solution, which proves to be far more computationally attractive than a batch approach. This deterministic technique is applied to the problem of audio signal segmentation, with simulations demonstrating the computational performance gain on toy data sets, and the accuracy of the segmentation on audio signals.

PostScript [BibTex]

PostScript [BibTex]


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The Kernel Mutual Information

Gretton, A., Herbrich, R., Smola, A.

In IEEE ICASSP Vol. 4, pages: 880-883, IEEE ICASSP, April 2003 (inproceedings)

Abstract
We introduce a new contrast function, the kernel mutual information (KMI), to measure the degree of independence of continuous random variables. This contrast function provides an approximate upper bound on the mutual information, as measured near independence, and is based on a kernel density estimate of the mutual information between a discretised approximation of the continuous random variables. We show that Bach and Jordan‘s kernel generalised variance (KGV) is also an upper bound on the same kernel density estimate, but is looser. Finally, we suggest that the addition of a regularising term in the KGV causes it to approach the KMI, which motivates the introduction of this regularisation.

PostScript [BibTex]

PostScript [BibTex]


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Blind separation of post-nonlinear mixtures using gaussianizing transformations and temporal decorrelation

Ziehe, A., Kawanabe, M., Harmeling, S., Müller, K.

In ICA 2003, pages: 269-274, (Editors: Amari, S.-I. , A. Cichocki, S. Makino, N. Murata), 4th International Symposium on Independent Component Analysis and Blind Signal Separation, April 2003 (inproceedings)

Abstract
At the previous workshop (ICA2001) we proposed the ACE-TD method that reduces the post-nonlinear blind source separation problem (PNL BSS) to a linear BSS problem. The method utilizes the Alternating Conditional Expectation (ACE) algorithm to approximately invert the (post-){non-linear} functions. In this contribution, we propose an alternative procedure called Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure yields similar results as the ACE method and can thus be used as a fast and effective equalization method. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations on realistic examples are performed to compare "Gauss-TD" with "ACE-TD".

PDF Web [BibTex]

PDF Web [BibTex]