O. Chapelle

发表

Improved Preconditioner for Hessian Free Optimization

O. Chapelle, 2011 .

SVMs for Histogram Based Image Classification

Vapnik, P. Haffner, O. Chapelle, 1999 .

Large-scale kernel machines

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Approximation Methods for Gaussian Process Regression

J. Weston, O. Chapelle, D. DeCoste, 2007 .

Cluster Kernels for Semi-Supervised Learning

Bernhard Schölkopf, Jason Weston, Olivier Chapelle, 2002, NIPS.

Object categorization with SVM: kernels for local features

J Eichhorn, Olivier Chapelle, O. Chapelle, 2004 .

Large margin optimization of ranking measures

O. Chapelle, 2007 .

Introduction to Semi-Supervised Learning

Bernhard Schölkopf, Alexander Zien, Olivier Chapelle, 2006, Semi-Supervised Learning.

Support vector machines for histogram-based image classification

Patrick Haffner, Olivier Chapelle, Vladimir Vapnik, 1999, IEEE Trans. Neural Networks.

An Empirical Evaluation of Thompson Sampling

Lihong Li, Olivier Chapelle, O. Chapelle, 2011, NIPS.

A reliable effective terascale linear learning system

John Langford, Miroslav Dudík, Alekh Agarwal, 2011, J. Mach. Learn. Res..

The 2005 PASCAL Visual Object Classes Challenge

Cordelia Schmid, Jorma Laaksonen, Luc Van Gool, 2005, MLCW.

Large margin taxonomy embedding with an application to document categorization

Kilian Q. Weinberger, O. Chapelle, 2008, NIPS 2008.

Semi-Supervised Learning

Alexander Zien, Olivier Chapelle, Bernhard Schlkopf, 2006 .

Scaling Learning Algorithms toward AI

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Support Vector Machine Solvers

J. Weston, L. Bottou, O. Chapelle, 2007 .

Choosing Multiple Parameters for Support Vector Machines

Sayan Mukherjee, Olivier Chapelle, Vladimir Vapnik, 2002, Machine Learning.

Tighter Bounds for Structured Estimation

Alexander J. Smola, Quoc V. Le, Choon Hui Teo, 2008, NIPS.

Learning to rank with (a lot of) word features

Yanjun Qi, Jason Weston, Bing Bai, 2010, Information Retrieval.

Bounds on error expectation for SVM

O. Chapelle, V. Vapnik, 2000 .

Training a Support Vector Machine in the Primal

Olivier Chapelle, O. Chapelle, 2007, Neural Computation.

Semi-Supervised Learning (Adaptive Computation and Machine Learning)

Bernhard Schölkopf, Alexander Zien, Olivier Chapelle, 2006 .

Kernel Dependency Estimation

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

Feature Selection for SVMs

Sayan Mukherjee, Jason Weston, Tomaso A. Poggio, 2000, NIPS.

Semi-Supervised Classification by Low Density Separation

Alexander Zien, Olivier Chapelle, O. Chapelle, 2005, AISTATS.

Supervised semantic indexing

Yanjun Qi, Jason Weston, Bing Bai, 2009, ECIR.

Building Support Vector Machines with Reduced Classifier Complexity

S. Sathiya Keerthi, Dennis DeCoste, Olivier Chapelle, 2006, J. Mach. Learn. Res..

Efficient algorithms for ranking with SVMs

S. Sathiya Keerthi, Olivier Chapelle, O. Chapelle, 2010, Information Retrieval.

A Machine Learning Approach to Conjoint Analysis

Zaïd Harchaoui, Olivier Chapelle, O. Chapelle, 2004, NIPS.

A dynamic bayesian network click model for web search ranking

Olivier Chapelle, Ya Zhang, O. Chapelle, 2009, WWW '09.

Multi-Class Feature Selection with Support Vector Machines

O. Chapelle, 2008 .

Second order optimization of kernel parameters

O. Chapelle, 2008 .

Model Selection for Support Vector Machines

Olivier Chapelle, Vladimir Vapnik, O. Chapelle, 1999, NIPS.

Transductive Inference for Estimating Values of Functions

Jason Weston, Olivier Chapelle, Vladimir Vapnik, 1999, NIPS.

Training Invariant SVMs Using Selective Sampling

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Large-Scale Kernel Machines (Neural Information Processing)

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Measure Based Regularization

Matthias Hein, Olivier Chapelle, Olivier Bousquet, 2003, NIPS.

Vicinal Risk Minimization

Jason Weston, Léon Bottou, Olivier Chapelle, 2000, NIPS.

Simple and Scalable Response Prediction for Display Advertising

Rómer Rosales, Olivier Chapelle, Eren Manavoglu, 2014, ACM Trans. Intell. Syst. Technol..

Bounds on Error Expectation for Support Vector Machines

V. Vapnik, O. Chapelle, O. Chapelle, 2000, Neural Computation.

Large-Scale Learning with String Kernels

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

A Comparison of Generative Models for Sequence Design

Lucy J. Colwell, O. Chapelle, B. Alipanahi, 2020 .

Biological Sequence Design using Batched Bayesian Optimization

Lucy J. Colwell, O. Chapelle, David Dohan, 2019 .

Classifier Cascade for Minimizing Feature Evaluation Cost

Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle, 2012, AISTATS.

Semi-supervised classification with hyperlinks

O. Chapelle, J. Abernethy, 2007 .

Incorporating invariances in nonlinear Support Vector Machines

Bernhard Schölkopf, Olivier Chapelle, B. Schölkopf, 2001, NIPS 2001.

A taxonomy of semi-supervised learning algorithms

Olivier Chapelle, O. Chapelle, 2005 .

No . TR-165 Learning with Transformation Invariant Kernels †

O. Chapelle, Christian J. Walder, 2007 .

Support Vector Machines et Classification d ’ Images

O. Chapelle, 1998 .

No . YR-2008-002 MULTI-CLASS FEATURE SELECTION WITH SUPPORT VECTOR MACHINES

O. Chapelle, 2008 .

Building SVMs with Reduced Classifier Complexity

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

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.

Incorporating Invariances in Non-Linear Support Vector Machines

Bernhard Schölkopf, Olivier Chapelle, B. Schölkopf, 2001, NIPS.

The Improved Fast Gauss Transform with Applications to Machine Learning

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

A Distributed Sequential Solver for Large-Scale SVMs

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Model Selection for Small Sample Regression

Yoshua Bengio, Olivier Chapelle, Vladimir Vapnik, 2002, Machine Learning.

Data cleaning algorithms with applications to micro-array experiments

Jason Weston, Isabelle Guyon, Olivier Chapelle, 2001 .

Support Vector Machines : principes d'induction, Réglage automatique et connaissances à priori

Olivier Chapelle, O. Chapelle, 2004 .

Notation and Symbols

Alexander Zien, Olivier Chapelle, Bernhard SchÃ¶lkopf, 2006 .

Label Propagation and Quadratic Criterion

Alexander Zien, Olivier Chapelle, Bernhard SchÃ¶lkopf, 2006 .

Estimating the Leave-One-Out Error for Classification Learning with SVMs

Arthur Gretton, Ralf Herbrich, Bernhard Schoelkopf, 2001 .

Spectral Methods for Dimensionality Reduction

O. Chapelle, A. Zien, Bernhard SchÃ¶lkopf, 2006 .

A Taxonomy for Semi-Supervised Learning Methods

Alexander Zien, Olivier Chapelle, Bernhard SchÃ¶lkopf, 2006 .

Semi-Supervised Learning in Practice

Alexander Zien, Olivier Chapelle, Bernhard SchÃ¶lkopf, 2006 .

Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge

Olivier Chapelle, O. Chapelle, 2002 .

Change of Representation

Alexander Zien, Olivier Chapelle, Bernhard SchÃ¶lkopf, 2006 .

Active Learning for Ranking through Expected Loss Optimization

Ya Zhang, Zhaohui Zheng, Yi Chang, 2010, IEEE Transactions on Knowledge and Data Engineering.

Active Learning for Parzen Window Classifier

Olivier Chapelle, O. Chapelle, 2005, AISTATS.

Boosted multi-task learning

Ya Zhang, Kilian Q. Weinberger, Belle L. Tseng, 2010, Machine Learning.

Feature selection for support vector machines by means of genetic algorithm

Bernhard Schölkopf, Holger Fröhlich, Olivier Chapelle, 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

Learning with Transformation Invariant Kernels

Christian Walder, Olivier Chapelle, O. Chapelle, 2007, NIPS.

An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models

S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle, 2006, NIPS.

Support Vector Machines Et Classiication D' Images Support Vector Machines for Image Classiication

O. Chapelle, 1998 .

Optimization Techniques for Semi-Supervised Support Vector Machines

S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle, 2008, J. Mach. Learn. Res..

Yahoo! Learning to Rank Challenge Overview

Yi Chang, Olivier Chapelle, O. Chapelle, 2010, Yahoo! Learning to Rank Challenge.

Embedded Methods

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

Branch and Bound for Semi-Supervised Support Vector Machines

S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle, 2006, NIPS.

Deterministic annealing for semi-supervised kernel machines

S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle, 2006, ICML.

A continuation method for semi-supervised SVMs

Alexander Zien, Mingmin Chi, Olivier Chapelle, 2006, ICML.

An Augmented PAC Model for Semi-Supervised Learning

Alexander Zien, Olivier Chapelle, Bernhard SchÃ¶lkopf, 2006 .

Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]

O. Chapelle, B. Scholkopf, A. Zien, Eds., 2009, IEEE Transactions on Neural Networks.

Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers

Alexander Zien, Olivier Chapelle, Bernhard SchÃ¶lkopf, 2006 .

Graph-Based Methods

Alexander Zien, Olivier Chapelle, Bernhard SchÃ¶lkopf, 2006 .

Graph regularization methods for Web spam detection

Carlos Castillo, Jacob D. Abernethy, Olivier Chapelle, 2010, Machine Learning.

Feature Selection for Support Vector Machines Using Genetic Algorithms

Bernhard Schölkopf, Holger Fröhlich, Olivier Chapelle, 2004, Int. J. Artif. Intell. Tools.

Estimating Predictive Variances with Kernel Ridge Regression

Gavin C. Cawley, Nicola L. C. Talbot, Olivier Chapelle, 2005, MLCW.

Large Margin Taxonomy Embedding for Document Categorization

Kilian Q. Weinberger, Olivier Chapelle, O. Chapelle, 2008, NIPS.

Expected reciprocal rank for graded relevance

Olivier Chapelle, Pierre Grinspan, Ya Zhang, 2009, CIKM.

Implicit surface modelling as an eigenvalue problem

Bernhard Schölkopf, Christian Walder, Olivier Chapelle, 2005, ICML.

Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions

Bernhard Schölkopf, Christian Walder, Olivier Chapelle, 2006, NIPS.

Large-scale validation and analysis of interleaved search evaluation

Filip Radlinski, Thorsten Joachims, Yisong Yue, 2012, TOIS.

Classifier cascades and trees for minimizing feature evaluation cost

Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, 2014, J. Mach. Learn. Res..

Intent-based diversification of web search results: metrics and algorithms

Emre Velipasaoglu, Larry Lai, Ciya Liao, 2011, Information Retrieval.

Modeling delayed feedback in display advertising

Olivier Chapelle, O. Chapelle, 2014, KDD.

Learning more powerful test statistics for click-based retrieval evaluation

Yue Gao, Thorsten Joachims, Ya Zhang, 2010, SIGIR.

Metric-Based Approaches for Semi-Supervised Regression and Classification

Alexander Zien, Olivier Chapelle, Bernhard SchÃ¶lkopf, 2006 .

An Analysis of Inference with the Universum

Bernhard Schölkopf, Alekh Agarwal, Olivier Chapelle, 2007, NIPS.

Early exit optimizations for additive machine learned ranking systems

Berkant Barla Cambazoglu, Zhaohui Zheng, Hugo Zaragoza, 2010, WSDM '10.

A General Boosting Method and its Application to Learning Ranking Functions for Web Search

Hongyuan Zha, Keke Chen, Zhaohui Zheng, 2007, NIPS.

Deterministic Annealing for Multiple-Instance Learning

Peter V. Gehler, Olivier Chapelle, O. Chapelle, 2007, AISTATS.

Multi-task learning for boosting with application to web search ranking

Ya Zhang, Kilian Q. Weinberger, Belle L. Tseng, 2010, KDD.

Semi-Supervised Learning by Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien (Review)

B. Schölkopf, O. Chapelle, A. Zien, Eds., 2009, IEEE Trans. Neural Networks.

The 2005 PASCAL Visual Object Classes Challenge

Christopher K. I. Williams, Jason D. R. Farquhar, Charles M. Bishop, 2005, MLCW.

Offline Evaluation of Response Prediction in Online Advertising Auctions

Olivier Chapelle, O. Chapelle, 2015, WWW.

Web spam identification through content and hyperlinks

Carlos Castillo, Jacob D. Abernethy, Olivier Chapelle, 2008, AIRWeb '08.

Gradient descent optimization of smoothed information retrieval metrics

Mingrui Wu, Olivier Chapelle, O. Chapelle, 2010, Information Retrieval.

Semi-Supervised Learning

O. Chapelle, A. Zien, Bernhard Schlkopf, 2006 .

Field-aware Factorization Machines in a Real-world Online Advertising System

Olivier Chapelle, Damien Lefortier, Yuchin Juan, 2017, WWW.

The Pascal Visual Object Classes Challenge 2006 ( VOC 2006 ) Results

Christopher K. I. Williams, Jason D. R. Farquhar, Charles M. Bishop, 2006 .

Future directions in learning to rank

Tie-Yan Liu, Yi Chang, Olivier Chapelle, 2010, Yahoo! Learning to Rank Challenge.

An Analysis of the Anti-learning Phenomenon for the Class Symmetric Polyhedron

Adam Kowalczyk, Olivier Chapelle, O. Chapelle, 2005, ALT.

Implicit Surface Modelling with a Globally Regularised Basis of Compact Support

Bernhard Schölkopf, Christian Walder, Olivier Chapelle, 2006, Comput. Graph. Forum.

Learning to suggest: a machine learning framework for ranking query suggestions

Umut Ozertem, Emre Velipasaoglu, Pinar Donmez, 2012, SIGIR '12.

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

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

Distance Metric Learning for Kernel Machines pdf

Kilian Q. Weinberger, Olivier Chapelle, Zhixiang Eddie Xu, 2012, ArXiv.

Second order optimization of kernel parameters

O. Chapelle, 2008 .

Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions

Olivier Chapelle, Flavian Vasile, Damien Lefortier, 2016, ADKDD@KDD.

The 2005 PASCAL Visual Object Classes Challenge

Christopher K. I. Williams, Jason D. R. Farquhar, Charles M. Bishop, 2005, MLCW.

Beyond binary relevance: preferences, diversity, and set-level judgments

Ben Carterette, Thorsten Joachims, Paul N. Bennett, 2008, SIGF.