ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A variety of kernel learning algorithms have been prop...
ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
We derive a generalization bound for multiclassification schemes based on grid clustering in categorical parameter product spaces. Grid clustering partitions the parameter space i...
ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
This paper introduces a novel machine learning model called multiple instance ranking (MIRank) that enables ranking to be performed in a multiple instance learning setting. The mo...
ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
Compressive sensing (CS) is an emerging field that, under appropriate conditions, can significantly reduce the number of measurements required for a given signal. In many applicat...
ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
We propose a new stopping condition for a Support Vector Machine (SVM) solver which precisely reflects the objective of the Leave-OneOut error computation. The stopping condition ...
ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
We examine the problem of evaluating a policy in the contextual bandit setting using only observations collected during the execution of another policy. We show that policy evalua...
ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
The problem of identifying the minimal gene set required to sustain life is of crucial importance in understanding cellular mechanisms and designing therapeutic drugs. This work d...
ICML
2008
IEEE
15 years 10 days ago
2008
IEEE
Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: ge...