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2001


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Pattern Selection Using the Bias and Variance of Ensemble

Shin, H., Cho, S.

In Proc. of the Korean Data Mining Conference, pages: 56-67, Korean Data Mining Conference, December 2001 (inproceedings)

[BibTex]

2001

[BibTex]


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Separation of post-nonlinear mixtures using ACE and temporal decorrelation

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

In ICA 2001, pages: 433-438, (Editors: Lee, T.-W. , T.P. Jung, S. Makeig, T. J. Sejnowski), Third International Workshop on Independent Component Analysis and Blind Signal Separation, December 2001 (inproceedings)

Abstract
We propose an efficient method based on the concept of maximal correlation that reduces the post-nonlinear blind source separation problem (PNL BSS) to a linear BSS problem. For this we apply the Alternating Conditional Expectation (ACE) algorithm – a powerful technique from nonparametric statistics – to approximately invert the (post-)nonlinear functions. Interestingly, in the framework of the ACE method convergence can be proven and in the PNL BSS scenario the optimal transformation found by ACE will coincide with the desired inverse functions. After the nonlinearities have been removed by ACE, temporal decorrelation (TD) allows us to recover the source signals. An excellent performance underlines the validity of our approach and demonstrates the ACE-TD method on realistic examples.

PDF [BibTex]

PDF [BibTex]


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Nonlinear blind source separation using kernel feature spaces

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

In ICA 2001, pages: 102-107, (Editors: Lee, T.-W. , T.P. Jung, S. Makeig, T. J. Sejnowski), Third International Workshop on Independent Component Analysis and Blind Signal Separation, December 2001 (inproceedings)

Abstract
In this work we propose a kernel-based blind source separation (BSS) algorithm that can perform nonlinear BSS for general invertible nonlinearities. For our kTDSEP algorithm we have to go through four steps: (i) adapting to the intrinsic dimension of the data mapped to feature space F, (ii) finding an orthonormal basis of this submanifold, (iii) mapping the data into the subspace of F spanned by this orthonormal basis, and (iv) applying temporal decorrelation BSS (TDSEP) to the mapped data. After demixing we get a number of irrelevant components and the original sources. To find out which ones are the components of interest, we propose a criterion that allows to identify the original sources. The excellent performance of kTDSEP is demonstrated in experiments on nonlinearly mixed speech data.

PDF [BibTex]

PDF [BibTex]


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Pattern Selection for ‘Regression’ using the Bias and Variance of Ensemble Network

Shin, H., Cho, S.

In Proc. of the Korean Institute of Industrial Engineers Conference, pages: 10-19, Korean Industrial Engineers Conference, November 2001 (inproceedings)

[BibTex]

[BibTex]


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Pattern Selection for ‘Classification’ using the Bias and Variance of Ensemble Neural Network

Shin, H., Cho, S.

In Proc. of the Korea Information Science Conference, pages: 307-309, Korea Information Science Conference, October 2001, Best Paper Award (inproceedings)

[BibTex]

[BibTex]


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Hybrid IDM/Impedance learning in human movements

Burdet, E., Teng, K., Chew, C., Peters, J., , B.

In ISHF 2001, 1, pages: 1-9, 1st International Symposium on Measurement, Analysis and Modeling of Human Functions (ISHF2001), September 2001 (inproceedings)

Abstract
In spite of motor output variability and the delay in the sensori-motor, humans routinely perform intrinsically un- stable tasks. The hybrid IDM/impedance learning con- troller presented in this paper enables skilful performance in strong stable and unstable environments. It consid- ers motor output variability identified from experimen- tal data, and contains two modules concurrently learning the endpoint force and impedance adapted to the envi- ronment. The simulations suggest how humans learn to skillfully perform intrinsically unstable tasks. Testable predictions are proposed.

PDF Web [BibTex]

PDF Web [BibTex]


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Combining Off- and On-line Calibration of a Digital Camera

Urbanek, M., Horaud, R., Sturm, P.

In In Proceedings of Third International Conference on 3-D Digital Imaging and Modeling, pages: 99-106, In Proceedings of Third International Conference on 3-D Digital Imaging and Modeling, June 2001 (inproceedings)

Abstract
We introduce a novel outlook on the self­calibration task, by considering images taken by a camera in motion, allowing for zooming and focusing. Apart from the complex relationship between the lens control settings and the intrinsic camera parameters, a prior off­line calibration allows to neglect the setting of focus, and to fix the principal point and aspect ratio throughout distinct views. Thus, the calibration matrix is dependent only on the zoom position. Given a fully calibrated reference view, one has only one parameter to estimate for any other view of the same scene, in order to calibrate it and to be able to perform metric reconstructions. We provide a close­form solution, and validate the reliability of the algorithm with experiments on real images. An important advantage of our method is a reduced ­ to one ­ number of critical camera configurations, associated with it. Moreover, we propose a method for computing the epipolar geometry of two views, taken from different positions and with different (spatial) resolutions; the idea is to take an appropriate third view, that is "easy" to match with the other two.

ZIP [BibTex]

ZIP [BibTex]


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Support vector novelty detection applied to jet engine vibration spectra

Hayton, P., Schölkopf, B., Tarassenko, L., Anuzis, P.

In Advances in Neural Information Processing Systems 13, pages: 946-952, (Editors: TK Leen and TG Dietterich and V Tresp), MIT Press, Cambridge, MA, USA, 14th Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
A system has been developed to extract diagnostic information from jet engine carcass vibration data. Support Vector Machines applied to novelty detection provide a measure of how unusual the shape of a vibration signature is, by learning a representation of normality. We describe a novel method for Support Vector Machines of including information from a second class for novelty detection and give results from the application to Jet Engine vibration analysis.

PDF Web [BibTex]

PDF Web [BibTex]


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Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm

Still, S., Schölkopf, B., Hepp, K., Douglas, R.

In Advances in Neural Information Processing Systems 13, pages: 741-747, (Editors: TK Leen and TG Dietterich and V Tresp), MIT Press, Cambridge, MA, USA, 14th Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
To control the walking gaits of a four-legged robot we present a novel neuromorphic VLSI chip that coordinates the relative phasing of the robot's legs similar to how spinal Central Pattern Generators are believed to control vertebrate locomotion [3]. The chip controls the leg movements by driving motors with time varying voltages which are the outputs of a small network of coupled oscillators. The characteristics of the chip's output voltages depend on a set of input parameters. The relationship between input parameters and output voltages can be computed analytically for an idealized system. In practice, however, this ideal relationship is only approximately true due to transistor mismatch and offsets.

PDF Web [BibTex]

PDF Web [BibTex]


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Algorithmic Stability and Generalization Performance

Bousquet, O., Elisseeff, A.

In Advances in Neural Information Processing Systems 13, pages: 196-202, (Editors: Leen, T.K. , T.G. Dietterich, V. Tresp), MIT Press, Cambridge, MA, USA, Fourteenth Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
We present a novel way of obtaining PAC-style bounds on the generalization error of learning algorithms, explicitly using their stability properties. A {\em stable} learner being one for which the learned solution does not change much for small changes in the training set. The bounds we obtain do not depend on any measure of the complexity of the hypothesis space (e.g. VC dimension) but rather depend on how the learning algorithm searches this space, and can thus be applied even when the VC dimension in infinite. We demonstrate that regularization networks possess the required stability property and apply our method to obtain new bounds on their generalization performance.

PDF Web [BibTex]

PDF Web [BibTex]


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The Kernel Trick for Distances

Schölkopf, B.

In Advances in Neural Information Processing Systems 13, pages: 301-307, (Editors: TK Leen and TG Dietterich and V Tresp), MIT Press, Cambridge, MA, USA, 14th Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearly related to the input space. This is done by identifying a class of kernels which can be represented as norm-based distances in Hilbert spaces. It turns out that the common kernel algorithms, such as SVMs and kernel PCA, are actually really distance based algorithms and can be run with that class of kernels, too. As well as providing a useful new insight into how these algorithms work, the present work can form the basis for conceiving new algorithms.

PDF Web [BibTex]

PDF Web [BibTex]


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Vicinal Risk Minimization

Chapelle, O., Weston, J., Bottou, L., Vapnik, V.

In Advances in Neural Information Processing Systems 13, pages: 416-422, (Editors: Leen, T.K. , T.G. Dietterich, V. Tresp), MIT Press, Cambridge, MA, USA, Fourteenth Annual Neural Information Processing Systems Conference (NIPS) , April 2001 (inproceedings)

Abstract
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector Machines or Statistical Regularization. We explain how VRM provides a framework which integrates a number of existing algorithms, such as Parzen windows, Support Vector Machines, Ridge Regression, Constrained Logistic Classifiers and Tangent-Prop. We then show how the approach implies new algorithms for solving problems usually associated with generative models. New algorithms are described for dealing with pattern recognition problems with very different pattern distributions and dealing with unlabeled data. Preliminary empirical results are presented.

PDF Web [BibTex]

PDF Web [BibTex]


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Feature Selection for SVMs

Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.

In Advances in Neural Information Processing Systems 13, pages: 668-674, (Editors: Leen, T.K. , T.G. Dietterich, V. Tresp), MIT Press, Cambridge, MA, USA, Fourteenth Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA microarray data.

PDF Web [BibTex]

PDF Web [BibTex]


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Occam’s Razor

Rasmussen, CE., Ghahramani, Z.

In Advances in Neural Information Processing Systems 13, pages: 294-300, (Editors: Leen, T.K. , T.G. Dietterich, V. Tresp), MIT Press, Cambridge, MA, USA, Fourteenth Annual Neural Information Processing Systems Conference (NIPS), April 2001 (inproceedings)

Abstract
The Bayesian paradigm apparently only sometimes gives rise to Occam's Razor; at other times very large models perform well. We give simple examples of both kinds of behaviour. The two views are reconciled when measuring complexity of functions, rather than of the machinery used to implement them. We analyze the complexity of functions for some linear in the parameter models that are equivalent to Gaussian Processes, and always find Occam's Razor at work.

PDF Web [BibTex]

PDF Web [BibTex]


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An Improved Training Algorithm for Kernel Fisher Discriminants

Mika, S., Schölkopf, B., Smola, A.

In Proceedings AISTATS, pages: 98-104, (Editors: T Jaakkola and T Richardson), Morgan Kaufman, San Francisco, CA, Artificial Intelligence and Statistics (AISTATS), January 2001 (inproceedings)

Web [BibTex]

Web [BibTex]


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Nonstationary Signal Classification using Support Vector Machines

Gretton, A., Davy, M., Doucet, A., Rayner, P.

In 11th IEEE Workshop on Statistical Signal Processing, pages: 305-305, 11th IEEE Workshop on Statistical Signal Processing, 2001 (inproceedings)

Abstract
In this paper, we demonstrate the use of support vector (SV) techniques for the binary classification of nonstationary sinusoidal signals with quadratic phase. We briefly describe the theory underpinning SV classification, and introduce the Cohen's group time-frequency representation, which is used to process the non-stationary signals so as to define the classifier input space. We show that the SV classifier outperforms alternative classification methods on this processed data.

PostScript [BibTex]

PostScript [BibTex]


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Enhanced User Authentication through Typing Biometrics with Artificial Neural Networks and K-Nearest Neighbor Algorithm

Wong, FWMH., Supian, ASM., Ismail, AF., Lai, WK., Ong, CS.

In 2001 (inproceedings)

[BibTex]

[BibTex]


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Predicting the Nonlinear Dynamics of Biological Neurons using Support Vector Machines with Different Kernels

Frontzek, T., Lal, TN., Eckmiller, R.

In Proceedings of the International Joint Conference on Neural Networks (IJCNN'2001) Washington DC, 2, pages: 1492-1497, Proceedings of the International Joint Conference on Neural Networks (IJCNN'2001) Washington DC, 2001 (inproceedings)

Abstract
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian, polynomial and tanh-kernels to learn and predict the nonlinear dynamics of single biological neurons. We show that SVMs for regression learn the dynamics of the pyloric dilator neuron of the australian crayfish, and we determine the optimal SVM parameters with regard to the test error. Compared to conventional RBF networks and MLPs, SVMs with gaussian kernels learned faster and performed a better iterated one-step-ahead prediction with regard to training and test error. From a biological point of view SVMs are especially better in predicting the most important part of the dynamics, where the membranpotential is driven by superimposed synaptic inputs to the threshold for the oscillatory peak.

PDF [BibTex]

PDF [BibTex]


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Computationally Efficient Face Detection

Romdhani, S., Torr, P., Schölkopf, B., Blake, A.

In Computer Vision, ICCV 2001, vol. 2, (73):695-700, IEEE, 8th International Conference on Computer Vision, 2001 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Design and Verification of Supervisory Controller of High-Speed Train

Yoo, SP., Lee, DY., Son, HI.

In IEEE International Symposium on Industrial Electronics, pages: 1290-1295, IEEE Operations Center, Piscataway, NJ, USA, IEEE International Symposium on Industrial Electronics (ISIE), 2001 (inproceedings)

Abstract
A high-level controller, supervisory controller, is required to monitor, control, and diagnose the low-level controllers of the high-speed train. The supervisory controller controls low-level controllers by monitoring input and output signals, events, and the high-speed train can be modeled as a discrete event system (DES). The high-speed train is modeled with automata, and the high-level control specification is defined. The supervisory controller is designed using the high-speed train model and the control specification. The designed supervisory controller is verified and evaluated with simulation using a computer-aided software engineering (CASE) tool, Object GEODE

Web DOI [BibTex]

Web DOI [BibTex]


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Towards Learning Path Planning for Solving Complex Robot Tasks

Frontzek, T., Lal, TN., Eckmiller, R.

In Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001) Vienna, pages: 943-950, Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001) Vienna, 2001 (inproceedings)

Abstract
For solving complex robot tasks it is necessary to incorporate path planning methods that are able to operate within different high-dimensional configuration spaces containing an unknown number of obstacles. Based on Advanced A*-algorithm (AA*) using expansion matrices instead of a simple expansion logic we propose a further improvement of AA* enabling the capability to learn directly from sample planning tasks. This is done by inserting weights into the expansion matrix which are modified according to a special learning rule. For an examplary planning task we show that Adaptive AA* learns movement vectors which allow larger movements than the initial ones into well-defined directions of the configuration space. Compared to standard approaches planning times are clearly reduced.

PDF [BibTex]

PDF [BibTex]


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Learning to predict the leave-one-out error of kernel based classifiers

Tsuda, K., Rätsch, G., Mika, S., Müller, K.

In International Conference on Artificial Neural Networks, ICANN'01, (LNCS 2130):331-338, (Editors: G. Dorffner, H. Bischof and K. Hornik), International Conference on Artificial Neural Networks, ICANN'01, 2001 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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A kernel approach for vector quantization with guaranteed distortion bounds

Tipping, M., Schölkopf, B.

In Artificial Intelligence and Statistics, pages: 129-134, (Editors: T Jaakkola and T Richardson), Morgan Kaufmann, San Francisco, CA, USA, 8th International Conference on Artificial Intelligence and Statistics (AI and STATISTICS), 2001 (inproceedings)

[BibTex]

[BibTex]


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Tracking a Small Set of Experts by Mixing Past Posteriors

Bousquet, O., Warmuth, M.

In Proceedings of the 14th Annual Conference on Computational Learning Theory, Lecture Notes in Computer Science, 2111, pages: 31-47, Proceedings of the 14th Annual Conference on Computational Learning Theory, Lecture Notes in Computer Science, 2001 (inproceedings)

Abstract
In this paper, we examine on-line learning problems in which the target concept is allowed to change over time. In each trial a master algorithm receives predictions from a large set of $n$ experts. Its goal is to predict almost as well as the best sequence of such experts chosen off-line by partitioning the training sequence into $k+1$ sections and then choosing the best expert for each section. We build on methods developed by Herbster and Warmuth and consider an open problem posed by Freund where the experts in the best partition are from a small pool of size $m$. Since $k>>m$ the best expert shifts back and forth between the experts of the small pool. We propose algorithms that solve this open problem by mixing the past posteriors maintained by the master algorithm. We relate the number of bits needed for encoding the best partition to the loss bounds of the algorithms. Instead of paying $\log n$ for choosing the best expert in each section we first pay $\log {n\choose m}$ bits in the bounds for identifying the pool of $m$ experts and then $\log m$ bits per new section. In the bounds we also pay twice for encoding the boundaries of the sections.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines

Frontzek, T., Lal, TN., Eckmiller, R.

In Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001), pages: 390-398, Proceedings of the International Conference on Artificial Neural Networks (ICANN'2001), 2001 (inproceedings)

Abstract
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We show that SVMs for regression learn the dynamics of the pyloric dilator neuron of the australian crayfish, and we determine the optimal SVM parameters with regard to the test error. Compared to conventional RBF networks, SVMs learned faster and performed a better iterated one-step-ahead prediction with regard to training and test error. From a biological point of view SVMs are especially better in predicting the most important part of the dynamics, where the membranpotential is driven by superimposed synaptic inputs to the threshold for the oscillatory peak.

PDF [BibTex]

PDF [BibTex]


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Estimating a Kernel Fisher Discriminant in the Presence of Label Noise

Lawrence, N., Schölkopf, B.

In 18th International Conference on Machine Learning, pages: 306-313, (Editors: CE Brodley and A Pohoreckyj Danyluk), Morgan Kaufmann , San Fransisco, CA, USA, 18th International Conference on Machine Learning (ICML), 2001 (inproceedings)

Web [BibTex]

Web [BibTex]


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A Generalized Representer Theorem

Schölkopf, B., Herbrich, R., Smola, A.

In Lecture Notes in Computer Science, Vol. 2111, (2111):416-426, LNCS, (Editors: D Helmbold and R Williamson), Springer, Berlin, Germany, Annual Conference on Computational Learning Theory (COLT/EuroCOLT), 2001 (inproceedings)

[BibTex]

[BibTex]


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Unsupervised Segmentation and Classification of Mixtures of Markovian Sources

Seldin, Y., Bejerano, G., Tishby, N.

In The 33rd Symposium on the Interface of Computing Science and Statistics (Interface 2001 - Frontiers in Data Mining and Bioinformatics), pages: 1-15, 33rd Symposium on the Interface of Computing Science and Statistics (Interface - Frontiers in Data Mining and Bioinformatics), 2001 (inproceedings)

Abstract
We describe a novel algorithm for unsupervised segmentation of sequences into alternating Variable Memory Markov sources, first presented in [SBT01]. The algorithm is based on competitive learning between Markov models, when implemented as Prediction Suffix Trees [RST96] using the MDL principle. By applying a model clustering procedure, based on rate distortion theory combined with deterministic annealing, we obtain a hierarchical segmentation of sequences between alternating Markov sources. The method is applied successfully to unsupervised segmentation of multilingual texts into languages where it is able to infer correctly both the number of languages and the language switching points. When applied to protein sequence families (results of the [BSMT01] work), we demonstrate the method‘s ability to identify biologically meaningful sub-sequences within the proteins, which correspond to signatures of important functional sub-units called domains. Our approach to proteins classification (through the obtained signatures) is shown to have both conceptual and practical advantages over the currently used methods.

PDF Web [BibTex]

PDF Web [BibTex]


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Support Vector Regression for Black-Box System Identification

Gretton, A., Doucet, A., Herbrich, R., Rayner, P., Schölkopf, B.

In 11th IEEE Workshop on Statistical Signal Processing, pages: 341-344, IEEE Signal Processing Society, Piscataway, NY, USA, 11th IEEE Workshop on Statistical Signal Processing, 2001 (inproceedings)

Abstract
In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box system identification. These methods derive from statistical learning theory, and are of great theoretical and practical interest. We briefly describe the theory underpinning SVR, and compare support vector methods with other approaches using radial basis networks. Finally, we apply SVR to modeling the behaviour of a hydraulic robot arm, and show that SVR improves on previously published results.

PostScript [BibTex]

PostScript [BibTex]


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Unsupervised Sequence Segmentation by a Mixture of Switching Variable Memory Markov Sources

Seldin, Y., Bejerano, G., Tishby, N.

In In the proceeding of the 18th International Conference on Machine Learning (ICML 2001), pages: 513-520, 18th International Conference on Machine Learning (ICML), 2001 (inproceedings)

Abstract
We present a novel information theoretic algorithm for unsupervised segmentation of sequences into alternating Variable Memory Markov sources. The algorithm is based on competitive learning between Markov models, when implemented as Prediction Suffix Trees (Ron et al., 1996) using the MDL principle. By applying a model clustering procedure, based on rate distortion theory combined with deterministic annealing, we obtain a hierarchical segmentation of sequences between alternating Markov sources. The algorithm seems to be self regulated and automatically avoids over segmentation. The method is applied successfully to unsupervised segmentation of multilingual texts into languages where it is able to infer correctly both the number of languages and the language switching points. When applied to protein sequence families, we demonstrate the method‘s ability to identify biologically meaningful sub-sequences within the proteins, which correspond to important functional sub-units called domains.

PDF [BibTex]

PDF [BibTex]


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Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation

Cheng, Y., Fu, Q., Gu, L., Li, S., Schölkopf, B., Zhang, H.

In Proceedings Computer Vision, 2001, Vol. 2, pages: 674-679, IEEE Computer Society, 8th International Conference on Computer Vision (ICCV), 2001 (inproceedings)

DOI [BibTex]

DOI [BibTex]

1999


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Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA

Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lemmen, C., Smola, A., Lengauer, T., Müller, K.

In German Conference on Bioinformatics (GCB 1999), October 1999 (inproceedings)

Abstract
In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding pro­ teins start, the so­called translation initiation sites (TIS). This can be modeled as a classification prob­ lem. We demonstrate the power of support vector machines (SVMs) for this task, and show how to suc­ cessfully incorporate biological prior knowledge by engineering an appropriate kernel function.

Web [BibTex]

1999

Web [BibTex]


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Shrinking the tube: a new support vector regression algorithm

Schölkopf, B., Bartlett, P., Smola, A., Williamson, R.

In Advances in Neural Information Processing Systems 11, pages: 330-336 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Semiparametric support vector and linear programming machines

Smola, A., Friess, T., Schölkopf, B.

In Advances in Neural Information Processing Systems 11, pages: 585-591 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, Twelfth Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

Abstract
Semiparametric models are useful tools in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. We extend two learning algorithms - Support Vector machines and Linear Programming machines to this case and give experimental results for SV machines.

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel PCA and De-noising in feature spaces

Mika, S., Schölkopf, B., Smola, A., Müller, K., Scholz, M., Rätsch, G.

In Advances in Neural Information Processing Systems 11, pages: 536-542 , (Editors: MS Kearns and SA Solla and DA Cohn), MIT Press, Cambridge, MA, USA, 12th Annual Conference on Neural Information Processing Systems (NIPS), June 1999 (inproceedings)

Abstract
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis. This gives rise to the question how to use nonlinear features for data compression, reconstruction, and de-noising, applications common in linear PCA. This is a nontrivial task, as the results provided by kernel PCA live in some high dimensional feature space and need not have pre-images in input space. This work presents ideas for finding approximate pre-images, focusing on Gaussian kernels, and shows experimental results using these pre-images in data reconstruction and de-noising on toy examples as well as on real world data.

PDF Web [BibTex]

PDF Web [BibTex]


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Classifying LEP data with support vector algorithms.

Vannerem, P., Müller, K., Smola, A., Schölkopf, B., Söldner-Rembold, S.

In Artificial Intelligence in High Energy Nuclear Physics 99, Artificial Intelligence in High Energy Nuclear Physics 99, 1999 (inproceedings)

[BibTex]

[BibTex]


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Classification on proximity data with LP-machines

Graepel, T., Herbrich, R., Schölkopf, B., Smola, A., Bartlett, P., Müller, K., Obermayer, K., Williamson, R.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 304-309, Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Kernel-dependent support vector error bounds

Schölkopf, B., Shawe-Taylor, J., Smola, A., Williamson, R.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 103-108 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Linear programs for automatic accuracy control in regression

Smola, A., Schölkopf, B., Rätsch, G.

In Artificial Neural Networks, 1999. ICANN 99, 470, pages: 575-580 , Conference Publications , IEEE, 9th International Conference on Artificial Neural Networks, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Regularized principal manifolds.

Smola, A., Williamson, R., Mika, S., Schölkopf, B.

In Lecture Notes in Artificial Intelligence, Vol. 1572, 1572, pages: 214-229 , Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

[BibTex]

[BibTex]


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Entropy numbers, operators and support vector kernels.

Williamson, R., Smola, A., Schölkopf, B.

In Lecture Notes in Artificial Intelligence, Vol. 1572, 1572, pages: 285-299, Lecture Notes in Artificial Intelligence, (Editors: P Fischer and H-U Simon), Springer, Berlin, Germany, Computational Learning Theory: 4th European Conference, 1999 (inproceedings)

[BibTex]

[BibTex]


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Is the Hippocampus a Kalman Filter?

Bousquet, O., Balakrishnan, K., Honavar, V.

In Proceedings of the Pacific Symposium on Biocomputing, 3, pages: 619-630, Proceedings of the Pacific Symposium on Biocomputing, 1999 (inproceedings)

[BibTex]

[BibTex]


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A Comparison of Artificial Neural Networks and Cluster Analysis for Typing Biometrics Authentication

Maisuria, K., Ong, CS., Lai, .

In unknown, pages: 9999-9999, International Joint Conference on Neural Networks, 1999 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Fisher discriminant analysis with kernels

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.

In Proceedings of the 1999 IEEE Signal Processing Society Workshop, 9, pages: 41-48, (Editors: Y-H Hu and J Larsen and E Wilson and S Douglas), IEEE, Neural Networks for Signal Processing IX, 1999 (inproceedings)

DOI [BibTex]

DOI [BibTex]