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Expected reciprocal rank for graded relevance
- O. Chapelle, D. Metlzer, Ya Zhang, P. Grinspan
- Computer ScienceInternational Conference on Information and…
- 2 November 2009
This work presents a new editorial metric for graded relevance which overcomes this difficulty and implicitly discounts documents which are shown below very relevant documents and calls it Expected Reciprocal Rank (ERR).
Choosing Multiple Parameters for Support Vector Machines
- O. Chapelle, V. Vapnik, O. Bousquet, Sayan Mukherjee
- Computer ScienceMachine-mediated learning
- 11 March 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…
An Empirical Evaluation of Thompson Sampling
Empirical results using Thompson sampling on simulated and real data are presented, and it is shown that it is highly competitive and should be part of the standard baselines to compare against.
Semi-Supervised Classification by Low Density Separation
Three semi-supervised algorithms are proposed: deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM, and optimizing the Transductive SVM objective function by gradient descent.
A dynamic bayesian network click model for web search ranking
A Dynamic Bayesian Network is proposed which aims at providing us with unbiased estimation of the relevance from the click logs and shows that the proposed click model outperforms other existing click models in predicting both click-through rate and relevance.
Training a Support Vector Machine in the Primal
- O. Chapelle
- Computer ScienceNeural Computation
- 1 May 2007
It is pointed 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.
Support vector machines for histogram-based image classification
It is observed that a simple remapping of the input x(i)-->x(i)(a) improves the performance of linear SVM's to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.
Feature Selection for SVMs
The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA microarray data.
Yahoo! Learning to Rank Challenge Overview
This paper provides an overview and an analysis of this challenge, along with a detailed description of the released datasets, used internally at Yahoo! for learning the web search ranking function.
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