Bernhard Schölkopf

Manfred K. Warmuth

This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.

16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings

Learning Theory and Kernel Machines

Martin T. Wells

Hongyu Zhao

Henry Horng-Shing Lu

Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

Handbook of Statistical Bioinformatics

Koji Tsuda

Director of the Max Planck Institute for Intelligent in Tübingen Germany Professor for Machine Lea Bernhard Schölkopf

Jean-Philippe Vert

A Primer on Molecular Biology. A Primer on Kernel Methods. Support Vector Machine Applications in Computational Biology. Inexact Matching String Kernels for Protein Classification. Fast Kernels for String and Tree Matching. Local Alignment Kernels for Biological Sequences. Kernels for Graphs. Diffusion Kernels. A Kernel for Protein Secondary Structure Prediction. Heterogeneous Data Comparsion and Gene Selection with Kernel Canonical Correlation Analysis. Kernel-Based Integration of Genomic Data Using Semdefinite Programming. Protein Classification via Kernel Matrix Completion. Accurate Splice Site Detection for Caenorhabditid elegans. Gene Espression Analysis: Joint Feature Selection and Classifier Design. Gene Selection for Microarray Data.

Kernel Methods in Computational Biology

Francis Bach

Alexander J. Smola

rnhard Schölkopf

Managing Director of the Max Planck Institute for Biological Cybernetics in Tubingen Germany Profe Bernhard Scholkopf

This volume provides an introduction to SVMs and related kernel methods. It provides concepts necessary to enable a reader to enter the world of machine learning using theoretical kernel algorithms and to understand and apply the algorithms that have been developed over the last few years.

Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels

Carl Edward Rasmussen

Heinrich H. Bülthoff

Martin A. Giese

This book constitutes the refereed proceedings of the 26th Symposium of the German Association for Pattern Recognition, DAGM 2004, held in Tübingen, Germany in August/September 2004. The 22 revised papers and 48 revised poster papers presented were carefully reviewed and selected from 146 submissions. The papers are organized in topical sections on learning, Bayesian approaches, vision and faces, vision and motion, biologically motivated approaches, segmentation, object recognition, and object recognition and synthesis.

26th DAGM Symposium, August 30 - September 1, 2004, Proceedings

Pattern Recognition

This book constitutes the refereed proceedings of the 26th Symposium of the German Association for Pattern Recognition, DAGM 2004, held in Tbingen, Germany in August/September 2004. The 22 revised papers and 48 revised poster papers presented were carefully reviewed and selected from 146 submissions. The papers are organized in topical sections on learning, Bayesian approaches, vision and faces, vision and motion, biologically motivated approaches, segmentation, object recognition, and object recognition and synthesis.

Thomas Hofmann

John Platt

The annual conference on NIPS is the flagship conference on neural computation. It draws top academic researchers from around the world & is considered to be a showcase conference for new developments in network algorithms & architectures. This volume contains all of the papers presented at NIPS 2006.

Proceedings of the 2006 Conference

Advances in Neural Information Processing Systems 19

Lawrence K. Saul

Sebastian Thrun

Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.

Proceedings of the 2003 Conference

Advances in Neural Information Processing Systems 16

Jonas Peters

Dominik Janzing

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Bernhard Schölkopf

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