André Elisseeff

发表

A comparative study of multi-class support vector machines in the unifying framework of large margin classifiers: Research Articles

Yann Guermeur, André Elisseeff, Dominique Zelus, 2005 .

Embedded Methods

Jason Weston, André Elisseeff, Olivier Chapelle, 2006, Feature Extraction.

Stability of Randomized Learning Algorithms

Massimiliano Pontil, Theodoros Evgeniou, André Elisseeff, 2005, J. Mach. Learn. Res..

A Stability Based Method for Discovering Structure in Clustered Data

Isabelle Guyon, André Elisseeff, Asa Ben-Hur, 2001, Pacific Symposium on Biocomputing.

Stability of ensembles of kernel machines

André Elisseeff, J. Vandewalle, G. Horvath, 2003 .

Kernel methods for Multi-labelled classification and Categ orical regression problems

Jason Weston, André Elisseeff, J. Weston, 2001, NIPS 2001.

A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables

André Elisseeff, Jean-Philippe Pellet, A. Elisseeff, 2007, IDA.

Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers

Massimiliano Pontil, André Elisseeff, Theodoros Evgeniou, 2004, Machine Learning.

Stability and Generalization

André Elisseeff, Olivier Bousquet, A. Elisseeff, 2002, J. Mach. Learn. Res..

AutoDL Challenge Design and Beta Tests-Towards automatic deep learning

Sergio Escalera, Quanming Yao, Wei-Wei Tu, 2018 .

Causal networks for risk and compliance: Methodology and application

André Elisseeff, Jean-Philippe Pellet, Eleni Pratsini, 2010, IBM J. Res. Dev..

JNN, a randomized algorithm for training multilayer networks in polynomial time

Hélène Paugam-Moisy, André Elisseeff, A. Elisseeff, 1999, Neurocomputing.

Explanation Trees for Causal Bayesian Networks pdf

André Elisseeff, Jean-Philippe Pellet, Ulf H. Nielsen, 2008, UAI.

Subagging for credit scoring models

Gianluca Antonini, André Elisseeff, Giuseppe Paleologo, 2010, Eur. J. Oper. Res..

Use of the Zero-Norm with Linear Models and Kernel Methods

Bernhard Schölkopf, Jason Weston, Michael E. Tipping, 2003, J. Mach. Learn. Res..

Guest editorial: Learning theory

André Elisseeff, Olivier Bousquet, A. Elisseeff, 2007, Machine Learning.

Semi-supervised Protein Classification Using Cluster Kernels

Jason Weston, William Stafford Noble, Christina S. Leslie, 2003, NIPS.

Structural bioinformatics Semi-supervised protein classification using cluster kernels

Jason Weston, William Stafford Noble, André Elisseeff, 2005 .

Kernel Dependency Estimation

Bernhard Schölkopf, Jason Weston, André Elisseeff, 2002, NIPS.

The 2005 ISMS Practice Prize Winner---Customer Equity and Lifetime Management (CELM) Finnair Case Study

André Elisseeff, Abderrahim Labbi, Seppo Pöyhönen, 2007 .

Algorithmic Stability and Model Selection for Bagging using Small Sub-samples.

Massimiliano Pontil, Theodoros Evgeniou, André Elisseeff, 2001, NIPS 2001.

Size of Multilayer Networks for Exact Learning: Analytic Approach

Hélène Paugam-Moisy, André Elisseeff, A. Elisseeff, 1996, NIPS.

A kernel method for multi-labelled classification

Jason Weston, André Elisseeff, J. Weston, 2001, NIPS.

A Simple Algorithm for Learning Stable Machines

Massimiliano Pontil, Theodoros Evgeniou, André Elisseeff, 2002, ECAI.

Combining protein secondary structure prediction models with ensemble methods of optimal complexity

Gianluca Pollastri, Hélène Paugam-Moisy, Yann Guermeur, 2004, Neurocomputing.

An Introduction to Feature Extraction

Isabelle Guyon, André Elisseeff, A. Elisseeff, 2006, Feature Extraction.

Algorithmic Stability and Generalization Performance

André Elisseeff, Olivier Bousquet, A. Elisseeff, 2000, NIPS.

An Introduction to Variable and Feature Selection

Isabelle Guyon, André Elisseeff, I. Guyon, 2003, J. Mach. Learn. Res..

Using Markov Blankets for Causal Structure Learning

André Elisseeff, Jean-Philippe Pellet, A. Elisseeff, 2008, J. Mach. Learn. Res..

Design and Analysis of the Causation and Prediction Challenge

Isabelle Guyon, Constantin F. Aliferis, Gregory F. Cooper, 2008, WCCI Causation and Prediction Challenge.

Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets

André Elisseeff, Jean-Philippe Pellet, A. Elisseeff, 2008, NIPS.

KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug Design-Binding to Thrombin

Bernhard Schölkopf, Jason Weston, André Elisseeff, 2001, Knowledge Discovery and Data Mining.

Jacobian Neural Network Learning Algorithms

André Elisseeff, Hélène Paugam-Moisy, A. Elisseeff, 1998 .

A new multi-class SVM based on a uniform convergence result

Hélène Paugam-Moisy, Yann Guermeur, André Elisseeff, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Efficient Allocation of Marketing Resources using Dynamic Programming

André Elisseeff, Abderrahim Labbi, Giuliano Tirenni, 2005, SDM.

Feature selection and transduction for prediction of molecular bioactivity for drug design

Bernhard Schölkopf, Jason Weston, Fernando Pérez-Cruz, 2003, Bioinform..

Estimating the sample complexity of a multi-class discriminant model

Hélène Paugam-Moisy, Yann Guermeur, André Elisseeff, 1999 .

Bounding the Capacity Measure of Multi-Class Discriminant Models

Yann Guermeur, André Elisseeff, Dominique Zelus, 2002 .

Partial Correlation-and Regression-Based Approaches to Causal Structure Learning Technical Report

André Elisseeff, Jean-Philippe Pellet, A. Elisseeff, 2007 .