Sciweavers

ICML
2005
IEEE
15 years 15 days ago
The cross entropy method for classification
We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the "L0 norm") as a regularizing term ...
Shie Mannor, Dori Peleg, Reuven Y. Rubinstein
ICML
2005
IEEE
15 years 15 days ago
Weighted decomposition kernels
We introduce a family of kernels on discrete data structures within the general class of decomposition kernels. A weighted decomposition kernel (WDK) is computed by dividing objec...
Sauro Menchetti, Fabrizio Costa, Paolo Frasconi
ICML
2005
IEEE
15 years 15 days ago
Logistic regression with an auxiliary data source
Xuejun Liao, Ya Xue, Lawrence Carin
ICML
2005
IEEE
15 years 15 days ago
Predicting relative performance of classifiers from samples
This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses th...
Rui Leite, Pavel Brazdil
ICML
2005
IEEE
15 years 15 days 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
15 years 15 days 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
15 years 15 days 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
15 years 15 days 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
15 years 15 days 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
15 years 15 days 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