Prototype Classification: Insights from Machine Learning (2009)
Graf, A.B.A., Bousquet, O., Rätsch, G., Schölkopf, B.
We shed light on the discrimination between patterns belonging to two different classes by casting this decoding problem into a generalized prototype framework. The discrimination process is then...
Statistical performance of support vector machines (2008)
Blanchard, G., Bousquet, O., Massart, P.
The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a...
Consistency of Spectral Clustering (2008)
Von Luxburg, U., Belkin, M., Bousquet, O.
Consistency is a key property of statistical algorithms when the data is drawn from some underlying probability distribution. Surprisingly, despite decades of work, little is known about consistency...
Statistical properties of kernel principal component analysis (2007)
Blanchard, G., Bousquet, O., Zwald, L.
The main goal of this paper is to prove inequalities on the reconstruction error for kernel principal component analysis. With respect to previous work on this topic, our contribution is twofold: (1)...
Evaluating predictive uncertainty challenge (2006)
Quinonero-Candela, J., Rasmussen, C.E., Sinz, F., Bousquet, O., Schölkopf, B.
This Chapter presents the PASCAL(1) Evaluating Predictive Uncertainty Challenge, introduces the contributed Chapters by the participants who obtained outstanding results, and provides a discussion...
Weston, J., Schölkopf, Bernhard, Bousquet, O.
We develop a methodology for solving high dimensional dependency estimation problems between pairs of data types, which is viable in the case where the output of interest has very high dimension,...
Maximal Margin Classification for Metric Spaces (2005)
Hein, M., Bousquet, O., Schölkopf, B.
In order to apply the maximum margin method in arbitrary metric spaces, we suggest to embed the metric space into a Banach or Hilbert space and to perform linear classification in this space. We...
Hilbertian metrics and positive definite kernels on probability measures (2005)
Hein, M., Bousquet, O., Ghahramani, Z., Cowell, R.
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good results in text...
Measuring Statistical Dependence with Hilbert-Schmidt Norms (2005)
Gretton, A., Bousquet, O., Smola, A., Schoelkopf, B.
We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm...
Moment Inequalities for Functions of Independent Random Variables (2005)
Boucheron, S., Bousquet, O., Lugosi, G., Massart, P.
A general method for obtaining moment inequalities for functions of independent random variables is presented. It is a generalization of the entropy method which has been used to derive concentration...
Weston, J., Schölkopf, B., Bousquet, O., Cabestany, J., Prieto, A., Sandoval, F., ...
We develop a methodology for solving high dimensional dependency estimation problems between pairs of data types, which is viable in the case where the output of interest has very high dimension,...
Local Rademacher Complexities (2005)
Bartlett, P., Bousquet, O., Mendelson, S.
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical...
Kernel Constrained Covariance for Dependence Measurement (2005)
Gretton, A., Smola, A.J., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., ...
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables....
Kernel Methods for Measuring Independence (2005)
Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the...
Theory of Classification: A Survey of Some Recent Advances (2005)
Boucheron, S., Bousquet, O., Lugosi, G.
The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have lead to these important...
Measure Based Regularization (2004)
Bousquet,O., Chapelle,O., Hein,M.
We address in this paper the question of how the knowledge of the marginal distribution $P(x)$ can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into...
Distance-Based Classification with Lipschitz Functions (2004)
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a...
Learning with Local and Global Consistency (2004)
Zhou,D., Bousquet,O., Lal,T.N., Weston,J., Schölkopf,B.
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised...
Ranking on Data Manifolds (2004)
Zhou,D., Weston,J., Gretton,A., Bousquet,O., Schölkopf,B.
The Google search engine has enjoyed a huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a...
Hilbertian Metrics on Probability Measures and their Application in SVM's (2004)
Hein,H., Lal,T.N., Bousquet,O.
The goal of this article is to investigate the field of Hilbertian metrics on probability measures. Since they are very versatile and can therefore be applied in various problems they are of great...
A Compression Approach to Support Vector Model Selection (2004)
Von Luxburg,U., Bousquet,O., Schölkopf,B.
In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization...
Statistical properties of kernel principal component analysis (2004)
Zwald, L., Bousquet, O., Blanchard, G.
We study the properties of the eigenvalues of Gram matrices in a non-asymptotic setting. Using local Rademacher averages, we provide data-dependent and tight bounds for their convergence towards...
Introduction to Statistical Learning Theory (2004)
Bousquet, O., Boucheron, S., Lugosi, G., Bousquet, O., Luxburg, U. Von, Rätsch, G.
Concentration Inequalities (2004)
Boucheron, S., Lugosi, G., Bousquet, O., Bousquet, O., Luxburg, U. Von, Rätsch, G.
Learning with Local and Global Consistency (2004)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B., Thrun, S., ...
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised...
Ranking on Data Manifolds (2004)
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B., Thrun, S., ...
The Google search engine has enjoyed a huge success with its web page ranking algorithm, which exploits global, rather than local, hyperlink structure of the web using random walks. Here we propose a...
Gaussian Processes in Machine Learning (2004)
Rasmussen, C.E., Bousquet, O., Luxburg, U. Von, Rätsch, G.
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present...
Hilbertian Metrics on Probability Measures and their Application in SVM's (2004)
Hein, H., Lal, T.N., Bousquet, O., Rasmussen, C. E., Bülthoff, H. H., Giese, M., ...
The goal of this article is to investigate the field of Hilbertian metrics on probability measures. Since they are very versatile and can therefore be applied in various problems they are of great...
Measure Based Regularization (2004)
Bousquet, O., Chapelle, O., Hein, M., Thrun, S., Saul, L., Schölkopf, B.
We address in this paper the question of how the knowledge of the marginal distribution $P(x)$ can be incorporated in a learning algorithm. We suggest three theoretical methods for taking into...
A Compression Approach to Support Vector Model Selection (2004)
Von Luxburg, U., Bousquet, O., Schölkopf, B.
In this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization...
Distance-Based Classification with Lipschitz Functions (2004)
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a...
Distance-based classification with Lipschitz functions (2003)
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a...
Feature selection and transduction for prediction of molecular bioactivity for drug design (2003)
Weston,J., Pérez-Cruz,F., Bousquet,O., Chapelle,O., Elisseeff,A., Schölkopf,B.
Distance-based classification with Lipschitz functions (2003)
Von Luxburg, U., Bousquet, O., Schölkopf, B., Warmuth, M.K.
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a...
Feature selection and transduction for prediction of molecular bioactivity for drug design (2003)
Weston, J., Pérez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., Schölkopf, B.
Concentration Inequalities for Sub-Additive Functions Using the Entropy Method (2003)
Bousquet, O., Giné, E., Houdré, C., Nualart, D.
We obtain exponential concentration inequalities for sub-additive functions of independent random variables under weak conditions on the increments of those functions, like the existence of...
New Approaches to Statistical Learning Theory (2003)
We present new tools from probability theory that can be applied to the analysis of learning algorithms. These tools allow to derive new bounds on the generalization performance of learning...
Application of an Inverse Design Method to the Design of Transonic Nacelles (1996)
Hepperle, M., Bartelheimer, W., Bousquet, O.
This paper describes the extension and application of a design method to nacelles for turbofan engines under transonic flow conditions. Starting form a generic nacelle shape and a prescribed pressure...