• Publications
  • Influence
Expected reciprocal rank for graded relevance
While numerous metrics for information retrieval are available in the case of binary relevance, there is only one commonly used metric for graded relevance, namely the Discounted Cumulative GainExpand
  • 677
  • 180
  • Open Access
Choosing Multiple Parameters for Support Vector Machines
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error ofExpand
  • 2,140
  • 131
  • Open Access
Introduction to Semi-Supervised Learning
This chapter contains sections titled: Supervised, Unsupervised, and Semi-Supervised Learning, When Can Semi-Supervised Learning Work?, Classes of Algorithms and Organization of This Book
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Training a Support Vector Machine in the Primal
  • O. Chapelle
  • Computer Science, Mathematics
  • Neural Computation
  • 1 May 2007
Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linearExpand
  • 742
  • 89
  • Open Access
Semi-Supervised Classification by Low Density Separation
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 lowExpand
  • 737
  • 86
  • Open Access
A dynamic bayesian network click model for web search ranking
As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide anExpand
  • 441
  • 82
  • Open Access
An Empirical Evaluation of Thompson Sampling
Thompson sampling is one of oldest heuristic to address the exploration / exploitation trade-off, but it is surprisingly unpopular in the literature. We present here some empirical results usingExpand
  • 807
  • 78
  • Open Access
Support vector machines for histogram-based image classification
Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM's)Expand
  • 1,358
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Feature Selection for SVMs
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 beExpand
  • 1,063
  • 72
  • Open Access
Yahoo! Learning to Rank Challenge Overview
Learning to rank for information retrieval has gained a lot of interest in the recent years but there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly releaseExpand
  • 393
  • 66
  • Open Access