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2002


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Microarrays: How Many Do You Need?

Zien, A., Fluck, J., Zimmer, R., Lengauer, T.

In RECOMB 2002, pages: 321-330, ACM Press, New York, NY, USA, Sixth Annual International Conference on Research in Computational Molecular Biology, April 2002 (inproceedings)

Abstract
We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. Current knowlegde seems to suffice for the construction of models that are realistic with respect to searches for individual differentially expressed genes. Such models allow to investigate the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class; the fold changes in expression; the detection sensitivity of the microarrays; and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at http://cartan.gmd.de/~zien/classsize/ for fine tuning simulations to their particular actualities. Since the situation can be assumed to be very similar for large scale proteomics and metabolomics studies, our methods and results might also apply there.

Web DOI [BibTex]

2002

Web DOI [BibTex]


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Nonlinear Multivariate Analysis with Geodesic Kernels

Kuss, M.

Biologische Kybernetik, Technische Universität Berlin, February 2002 (diplomathesis)

GZIP [BibTex]

GZIP [BibTex]


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Pattern Selection for Support Vector Classifiers

Shin, H., Cho, S.

In Ideal 2002, pages: 97-103, (Editors: Yin, H. , N. Allinson, R. Freeman, J. Keane, S. Hubbard), Springer, Berlin, Germany, Third International Conference on Intelligent Data Engineering and Automated Learning, January 2002 (inproceedings)

Abstract
SVMs tend to take a very long time to train with a large data set. If "redundant" patterns are identified and deleted in pre-processing, the training time could be reduced significantly. We propose a k-nearest neighbors(k-NN) based pattern selection method. The method tries to select the patterns that are near the decision boundary and that are correctly labeled. The simulations over synthetic data sets showed promising results: (1) By converting a non-separable problem to a separable one, the search for an optimal error tolerance parameter became unnecessary. (2) SVM training time decreased by two orders of magnitude without any loss of accuracy. (3) The redundant SVs were substantially reduced.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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The leave-one-out kernel

Tsuda, K., Kawanabe, M.

In Artificial Neural Networks -- ICANN 2002, 2415, pages: 727-732, LNCS, (Editors: Dorronsoro, J. R.), Artificial Neural Networks -- ICANN, 2002 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms

Bousquet, O.

Biologische Kybernetik, Ecole Polytechnique, 2002 (phdthesis) Accepted

Abstract
New classification algorithms based on the notion of 'margin' (e.g. Support Vector Machines, Boosting) have recently been developed. The goal of this thesis is to better understand how they work, via a study of their theoretical performance. In order to do this, a general framework for real-valued classification is proposed. In this framework, it appears that the natural tools to use are Concentration Inequalities and Empirical Processes Theory. Thanks to an adaptation of these tools, a new measure of the size of a class of functions is introduced, which can be computed from the data. This allows, on the one hand, to better understand the role of eigenvalues of the kernel matrix in Support Vector Machines, and on the other hand, to obtain empirical model selection criteria.

PostScript [BibTex]


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Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge

Chapelle, O.

Biologische Kybernetik, 2002 (phdthesis)

Abstract
This thesis presents a theoretical and practical study of Support Vector Machines (SVM) and related learning algorithms. In a first part, we introduce a new induction principle from which SVMs can be derived, but some new algorithms are also presented in this framework. In a second part, after studying how to estimate the generalization error of an SVM, we suggest to choose the kernel parameters of an SVM by minimizing this estimate. Several applications such as feature selection are presented. Finally the third part deals with the incoporation of prior knowledge in a learning algorithm and more specifically, we studied the case of known invariant transormations and the use of unlabeled data.

GZIP [BibTex]


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Localized Rademacher Complexities

Bartlett, P., Bousquet, O., Mendelson, S.

In Proceedings of the 15th annual conference on Computational Learning Theory, pages: 44-58, Proceedings of the 15th annual conference on Computational Learning Theory, 2002 (inproceedings)

Abstract
We investigate the behaviour of global and local Rademacher averages. We present new error bounds which are based on the local averages and indicate how data-dependent local averages can be estimated without {it a priori} knowledge of the class at hand.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Film Cooling: A Comparative Study of Different Heaterfoil Configurations for Liquid Crystals Experiments

Vogel, G., Graf, ABA., Weigand, B.

In ASME TURBO EXPO 2002, Amsterdam, GT-2002-30552, ASME TURBO EXPO, Amsterdam, 2002 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Some Local Measures of Complexity of Convex Hulls and Generalization Bounds

Bousquet, O., Koltchinskii, V., Panchenko, D.

In Proceedings of the 15th annual conference on Computational Learning Theory, Proceedings of the 15th annual conference on Computational Learning Theory, 2002 (inproceedings)

Abstract
We investigate measures of complexity of function classes based on continuity moduli of Gaussian and Rademacher processes. For Gaussian processes, we obtain bounds on the continuity modulus on the convex hull of a function class in terms of the same quantity for the class itself. We also obtain new bounds on generalization error in terms of localized Rademacher complexities. This allows us to prove new results about generalization performance for convex hulls in terms of characteristics of the base class. As a byproduct, we obtain a simple proof of some of the known bounds on the entropy of convex hulls.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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A kernel approach for learning from almost orthogonal patterns

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

In Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science, 2430/2431, pages: 511-528, Lecture Notes in Computer Science, (Editors: T Elomaa and H Mannila and H Toivonen), Springer, Berlin, Germany, 13th European Conference on Machine Learning (ECML) and 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'2002), 2002 (inproceedings)

PostScript DOI [BibTex]

PostScript DOI [BibTex]


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Infinite Mixtures of Gaussian Process Experts

Rasmussen, CE., Ghahramani, Z.

In (Editors: Dietterich, Thomas G.; Becker, Suzanna; Ghahramani, Zoubin), 2002 (inproceedings)

Abstract
We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using a input-dependent adaptation of the Dirichlet Process, we implement a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets -- thus potentially overcoming two of the biggest hurdles with GP models. Simulations show the viability of this approach.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Marginalized kernels for RNA sequence data analysis

Kin, T., Tsuda, K., Asai, K.

In Genome Informatics 2002, pages: 112-122, (Editors: Lathtop, R. H.; Nakai, K.; Miyano, S.; Takagi, T.; Kanehisa, M.), Genome Informatics, 2002, (Best Paper Award) (inproceedings)

Web [BibTex]

Web [BibTex]


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Luminance Artifacts on CRT Displays

Wichmann, F.

In IEEE Visualization, pages: 571-574, (Editors: Moorhead, R.; Gross, M.; Joy, K. I.), IEEE Visualization, 2002 (inproceedings)

Abstract
Most visualization panels today are still built around cathode-ray tubes (CRTs), certainly on personal desktops at work and at home. Whilst capable of producing pleasing images for common applications ranging from email writing to TV and DVD presentation, it is as well to note that there are a number of nonlinear transformations between input (voltage) and output (luminance) which distort the digital and/or analogue images send to a CRT. Some of them are input-independent and hence easy to fix, e.g. gamma correction, but others, such as pixel interactions, depend on the content of the input stimulus and are thus harder to compensate for. CRT-induced image distortions cause problems not only in basic vision research but also for applications where image fidelity is critical, most notably in medicine (digitization of X-ray images for diagnostic purposes) and in forms of online commerce, such as the online sale of images, where the image must be reproduced on some output device which will not have the same transfer function as the customer's CRT. I will present measurements from a number of CRTs and illustrate how some of their shortcomings may be problematic for the aforementioned applications.

[BibTex]

[BibTex]

1996


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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]


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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]


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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]


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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]


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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]


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Evaluation of Gaussian Processes and other Methods for Non-Linear Regression

Rasmussen, CE.

Biologische Kybernetik, Graduate Department of Computer Science, Univeristy of Toronto, 1996 (phdthesis)

PostScript [BibTex]

PostScript [BibTex]