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ML
2002
ACM
140views Machine Learning» more  ML 2002»
13 years 4 months ago
A Probabilistic Framework for SVM Regression and Error Bar Estimation
In this paper, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM) under some convex assumptions for the loss functions. ...
Junbin Gao, Steve R. Gunn, Chris J. Harris, Martin...
ML
2002
ACM
167views Machine Learning» more  ML 2002»
13 years 4 months ago
Linear Programming Boosting via Column Generation
We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation based simplex method. We formulate the problem as...
Ayhan Demiriz, Kristin P. Bennett, John Shawe-Tayl...
ML
2002
ACM
107views Machine Learning» more  ML 2002»
13 years 4 months ago
Training Invariant Support Vector Machines
Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated...
Dennis DeCoste, Bernhard Schölkopf
ML
2002
ACM
100views Machine Learning» more  ML 2002»
13 years 4 months ago
Structure in the Space of Value Functions
Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental ...
David J. Foster, Peter Dayan
ML
2002
ACM
135views Machine Learning» more  ML 2002»
13 years 4 months ago
Bayesian Treed Models
When simple parametric models such as linear regression fail to adequately approximate a relationship across an entire set of data, an alternative may be to consider a partition o...
Hugh A. Chipman, Edward I. George, Robert E. McCul...
ML
2002
ACM
121views Machine Learning» more  ML 2002»
13 years 4 months ago
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 gener...
Olivier Chapelle, Vladimir Vapnik, Olivier Bousque...
ML
2002
ACM
129views Machine Learning» more  ML 2002»
13 years 4 months ago
Model Selection for Small Sample Regression
Model selection is an important ingredient of many machine learning algorithms, in particular when the sample size in small, in order to strike the right trade-off between overfitt...
Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio
ML
2002
ACM
111views Machine Learning» more  ML 2002»
13 years 4 months ago
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data
Binningandtruncationofdataarecommonindataanalysisandmachinelearning.Thispaperaddresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM ap...
Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachl...
ML
2002
ACM
133views Machine Learning» more  ML 2002»
13 years 4 months ago
Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates
For two-class datasets, we provide a method for estimating the generalization error of a bag using out-of-bag estimates. In bagging, each predictor (single hypothesis) is learned ...
Tom Bylander
ML
2002
ACM
133views Machine Learning» more  ML 2002»
13 years 4 months ago
Finite-time Analysis of the Multiarmed Bandit Problem
Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while t...
Peter Auer, Nicolò Cesa-Bianchi, Paul Fisch...