Eurographics E. Gröller, L. Szirmay-kalos, C. Walder, B. Schölkopf, O. Chapelle
We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The...
An Analysis of Inference with the Universum (2008)
Sinz, F.H., Chapelle, O., Agarwal, A., Schölkopf, B.
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative...
An Analysis of Inference with the Universum (2008)
Sinz, F.H., Chapelle, O., Agarwal, A., Schölkopf, B., Platt, J. C., Koller, D., ...
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative...
Learning with Transformation Invariant Kernels (2008)
Walder, C., Chapelle, O., Platt, J. C., Koller, D., Singer, Y., Roweis, S.
This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation invariant, only...
An Analysis of Inference with the Universum (2007)
Sinz, F.H., Chapelle, O., Agarwal, A., Schölkopf, B.
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative...
Training a Support Vector Machine in the Primal (2007)
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both...
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions (2007)
Walder, C., Schölkopf, B., Chapelle, O.
We consider the problem of constructing a function whose zero set is to represent a surface, given sample points with surface normal vectors. The contributions include a novel means of regularising...
An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models (2007)
Keerthi, S.S., Sindhwani, V., Chapelle, O.
We consider the task of tuning hyperparameters in SVM models based on minimizing a smooth performance validation function, e.g., smoothed k-fold cross-validation error, using non-linear optimization...
Deterministic Annealing for Multiple-Instance Learning (2007)
In this paper we demonstrate how deterministic annealing can be applied to different SVM formulations of the multiple-instance learning (MIL) problem. Our results show that we find better local...
Branch and Bound for Semi-Supervised Support Vector Machines (2007)
Chapelle, O., Sindhwani, V., Keerthi, S.S.
Semi-supervised SVMs (S3VMs) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimization problem is non-convex. To examine the...
To appear in “Large-Scale Kernel Machines”, (2007)
Yoshua Bengio, Yann Lecun, L. Bottou, O. Chapelle, ...
One long-term goal of machine learning research is to produce methods that are applicable to highly complex tasks, such as perception (vision, audition), reasoning, intelligent control, and other...
To appear in “Large-Scale Kernel Machines”, (2007)
Yoshua Bengio, Yann Lecun, L. Bottou, O. Chapelle, ...
One long-term goal of machine learning research is to produce methods that are applicable to highly complex tasks, such as perception (vision, audition), reasoning, intelligent control, and other...
To appear in “Large-Scale Kernel Machines”, (2007)
Yoshua Bengio, Yann Lecun, L. Bottou, O. Chapelle, ...
One long-term goal of machine learning research is to produce methods that are applicable to highly complex tasks, such as perception (vision, audition), reasoning, intelligent control, and other...
Implicit Surface Modelling with a Globally Regularised Basis of Compact Support (2006)
Walder, C, Schoelkopf, B, Chapelle, O
We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The...
Lal, T.N., Chapelle, O., Weston, J., Elisseeff, A., Guyon, I., Gunn, S., ...
Embedded methods are a relatively new approach to feature selection. Unlike filter methods, which do not incorporate learning, and wrapper approaches, which can be used with arbitrary classifiers, in...
Combining a Filter Method with SVMs (2006)
Lal, T.N., Chapelle, O., Schölkopf, B., Guyon, I., Gunn, S., Nikravesh, M., ...
Our goal for the competition (feature selection competition NIPS 2003) was to evaluate the usefulness of simple machine learning techniques. We decided to use the correlation criteria as a feature...
Estimating Predictive Variances with Kernel Ridge Regression (2006)
Cawley, G.C., Talbot, N.L.C., Chapelle, O.
In many regression tasks, in addition to an accurate estimate of the conditional mean of the target distribution, an indication of the predictive uncertainty is also required. There are two principal...
Deterministic Annealing for Semi-supervised Kernel Machines (2006)
Sindhwani, V., Keerthi, S., Chapelle, O.
An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat the unknown labels as additional optimization variables. For margin-based loss...
A Continuation Method for Semi-Supervised SVMs (2006)
Chapelle, O., Chi, M., Zien, A.
Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters....
Implicit Surface Modelling with a Globally Regularised Basis of Compact Support (2006)
Walder, C., Schölkopf, B., Chapelle, O.
We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The...
Building Support Vector Machines with Reduced Classifier Complexity (2006)
Keerthi, S., Chapelle, O., Decoste, D.
Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we...
Semi-Supervised Learning (2006)
Chapelle, O., Schölkopf, B., Zien, A.
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in...
Active Learning for Parzen Window Classifier (2005)
The problem of active learning is approached in this paper by minimizing directly an estimate of the expected test error. The main difficulty in this ``optimal'' strategy is that output probabilities...
Semi-Supervised Classification by Low Density Separation (2005)
We believe that the cluster assumption is key to successful semi-supervised learning. Based on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances that emphazise low...
An Analysis of the Anti-Learning Phenomenon for the Class Symmetric Polyhedron (2005)
This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on the test set. This...
Implicit Surface Modelling as an Eigenvalue Problem (2005)
Walder, C., Chapelle, O., Schölkopf, B., Raedt, L. De, Wrobel, S.
We discuss the problem of fitting an implicit shape model to a set of points sampled from a co-dimension one manifold of arbitrary topology. The method solves a non-convex optimisation problem in the...
A Machine Learning Approach to Conjoint Analysis (2005)
Chapelle, O., Harchaoui, Z., Saul, L.K., Weiss, Y., Bottou, L.
Choice-based conjoint analysis builds models of consumers preferences over products with answers gathered in questionnaires. Our main goal is to bring tools from the machine learning community to...
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...
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...
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.
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.
Feature Selection for SVMs (2001)
J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, Tomaso Poggio, Vladimir Vapnik
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error, which can be efficiently...
Feature selection for SVMs (2000)
J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be...
Feature selection for SVMs (2000)
J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be...