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ICML
2005
IEEE
9 years 10 months ago
Heteroscedastic Gaussian process regression
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance l...
Alexander J. Smola, Quoc V. Le, Stéphane Ca...
ICML
2005
IEEE
9 years 10 months ago
PAC-Bayes risk bounds for sample-compressed Gibbs classifiers
We extend the PAC-Bayes theorem to the sample-compression setting where each classifier is represented by two independent sources of information: a compression set which consists ...
François Laviolette, Mario Marchand
ICML
2005
IEEE
9 years 10 months ago
Relating reinforcement learning performance to classification performance
We prove a quantitative connection between the expected sum of rewards of a policy and binary classification performance on created subproblems. This connection holds without any ...
John Langford, Bianca Zadrozny
ICML
2005
IEEE
9 years 10 months ago
Using additive expert ensembles to cope with concept drift
We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence ...
Jeremy Z. Kolter, Marcus A. Maloof
ICML
2005
IEEE
9 years 10 months ago
A brain computer interface with online feedback based on magnetoencephalography
The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG)...
Bernhard Schölkopf, Hubert Preißl, J&uu...
ICML
2005
IEEE
9 years 10 months ago
Learning the structure of Markov logic networks
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
Stanley Kok, Pedro Domingos
ICML
2005
IEEE
9 years 10 months ago
Computational aspects of Bayesian partition models
The conditional distribution of a discrete variable y, given another discrete variable x, is often specified by assigning one multinomial distribution to each state of x. The cost...
Mikko Koivisto, Kismat Sood
ICML
2005
IEEE
9 years 10 months ago
Semi-supervised graph clustering: a kernel approach
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are...
Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Ray...
ICML
2005
IEEE
9 years 10 months ago
Generalized LARS as an effective feature selection tool for text classification with SVMs
In this paper we generalize the LARS feature selection method to the linear SVM model, derive an efficient algorithm for it, and empirically demonstrate its usefulness as a featur...
S. Sathiya Keerthi
ICML
2005
IEEE
9 years 10 months ago
A comparison of tight generalization error bounds
We investigate the empirical applicability of several bounds (a number of which are new) on the true error rate of learned classifiers which hold whenever the examples are chosen ...
John Langford, Matti Kääriäinen
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