Header logo is ei


2001


no image
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]

2001

PDF [BibTex]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]


no image
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]

1996


no image
Quality Prediction of Steel Products using Neural Networks

Shin, H., Jhee, W.

In Proc. of the Korean Expert System Conference, pages: 112-124, Korean Expert System Society Conference, November 1996 (inproceedings)

[BibTex]

1996

[BibTex]


no image
Comparison of view-based object recognition algorithms using realistic 3D models

Blanz, V., Schölkopf, B., Bülthoff, H., Burges, C., Vapnik, V., Vetter, T.

In Artificial Neural Networks: ICANN 96, LNCS, vol. 1112, pages: 251-256, Lecture Notes in Computer Science, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996 (inproceedings)

Abstract
Two view-based object recognition algorithms are compared: (1) a heuristic algorithm based on oriented filters, and (2) a support vector learning machine trained on low-resolution images of the objects. Classification performance is assessed using a high number of images generated by a computer graphics system under precisely controlled conditions. Training- and test-images show a set of 25 realistic three-dimensional models of chairs from viewing directions spread over the upper half of the viewing sphere. The percentage of correct identification of all 25 objects is measured.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


no image
Incorporating invariances in support vector learning machines

Schölkopf, B., Burges, C., Vapnik, V.

In Artificial Neural Networks: ICANN 96, LNCS vol. 1112, pages: 47-52, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996, volume 1112 of Lecture Notes in Computer Science (inproceedings)

Abstract
Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
A practical Monte Carlo implementation of Bayesian learning

Rasmussen, CE.

In Advances in Neural Information Processing Systems 8, pages: 598-604, (Editors: Touretzky, D.S. , M.C. Mozer, M.E. Hasselmo), MIT Press, Cambridge, MA, USA, Ninth Annual Conference on Neural Information Processing Systems (NIPS), June 1996 (inproceedings)

Abstract
A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte Carlo methods is presented and evaluated. In reasonably small amounts of computer time this approach outperforms other state-of-the-art methods on 5 datalimited tasks from real world domains.

PDF Web [BibTex]

PDF Web [BibTex]


no image
Gaussian Processes for Regression

Williams, CKI., Rasmussen, CE.

In Advances in neural information processing systems 8, pages: 514-520, (Editors: Touretzky, D.S. , M.C. Mozer, M.E. Hasselmo), MIT Press, Cambridge, MA, USA, Ninth Annual Conference on Neural Information Processing Systems (NIPS), June 1996 (inproceedings)

Abstract
The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior over functions. We investigate the use of a Gaussian process prior over functions, which permits the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.

PDF Web [BibTex]

PDF Web [BibTex]