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- Olivier Chapelle, Donald Metlzer, Ya Zhang, Pierre Grinspan
- CIKM
- 2009

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 Gain (DCG). A drawback of DCG is its additive nature and the underlying independence assumption: a document in a given position has always the same gain and discount… (More)

- Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
- Machine Learning
- 2002

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 of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon… (More)

- Olivier Chapelle
- Neural Computation
- 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 linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the primal point of view, new families of algorithms for… (More)

- Olivier Chapelle, Alexander Zien
- AISTATS
- 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 density regions between clusters, followed by training a standard SVM; 2. optimizing the Transductive SVM objective function, which places the decision boundary… (More)

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 efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life… (More)

- Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford
- Journal of Machine Learning Research
- 2014

We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features, 1 billions of training examples and millions of parameters in an hour using a cluster of 1000 machines. Individually none of the component techniques is new, but the careful synthesis required to obtain an… (More)

- Olivier Chapelle, Ya Zhang
- WWW
- 2009

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 an important source of implicit feedback and can be used as a cheap proxy for editorial labels. The main difficulty however comes from the so called position bias -… (More)

- Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi
- Journal of Machine Learning Research
- 2008

Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S 3 VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A… (More)

- Olivier Chapelle, Yi Chang
- Yahoo! Learning to Rank Challenge
- 2011

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 release two datasets used internally at Yahoo! for learning the web search ranking function. To promote these datasets and foster the development of state-of-the-art… (More)

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

Semi-Supervised Support Vector Machines (S<sup>3</sup>VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters. However their main problem is that the optimization problem is non-convex and has many <i>local minima</i>, which often results in suboptimal… (More)