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- Isabelle Guyon, André Elisseeff
- Journal of Machine Learning Research
- 2003

Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving… (More)

- Olivier Bousquet, André Elisseeff
- Journal of Machine Learning Research
- 2002

We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real-valued function. We… (More)

We introduce the notion of kernel-alignment, a measure of similarity between two kernel functions or between a kernel and a target function. This quantity captures the degree of agreement between a kernel and a given learning task, and has very natural interpretations in machine learning, leading also to simple algorithms for model selection and learning.… (More)

- André Elisseeff, Jason Weston
- NIPS
- 2001

This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed into many two-class problems but the expressive power of such a system can be weak [5, 7]. We explore a new direct approach. It is based on a large margin ranking system that shares a lot of common properties with… (More)

- Jason Weston, André Elisseeff, Bernhard Schölkopf, Michael E. Tipping
- Journal of Machine Learning Research
- 2003

We explore the use of the so-called zero-norm of the parameters of linear models in learning. Minimization of such a quantity has many uses in a machine learning context: for variable or feature selection, minimizing training error and ensuring sparsity in solutions. We derive a simple but practical method for achieving these goals and discuss its… (More)

- Asa Ben-Hur, André Elisseeff, Isabelle Guyon
- Pacific Symposium on Biocomputing
- 2002

We present a method for visually and quantitatively assessing the presence of structure in clustered data. The method exploits measurements of the stability of clustering solutions obtained by perturbing the data set. Stability is characterized by the distribution of pairwise similarities between clusterings obtained from sub samples of the data. High… (More)

We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using kernel functions, thus embedding… (More)

This report presents a SVM like learning system to handle multi-label problems. Such problems arise naturally in bio-informatics. Consider for instance the MIPS Yeast genome database in [12], it is formed by around 3,300 genes associated to their functional classes. One gene can have many classes, and different genes do not belong to the same number of… (More)

- Olivier Bousquet, André Elisseeff
- NIPS
- 2000

We present a novel way of obtaining PAC-style bounds on the generalization error of learning algorithms, explicitly using their stability properties. A stable learner is one for which the learned solution does not change much with small changes in the training set. The bounds we obtain do not depend on any measure of the complexity of the hypothesis space… (More)

MOTIVATION
Building an accurate protein classification system depends critically upon choosing a good representation of the input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art classification performance. However, such representations are based only on labeled data--examples with known 3D… (More)