ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We consider the problem of extracting informative exemplars from a data stream. Examples of this problem include exemplarbased clustering and nonparametric inference such as Gauss...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Learning temporal causal graph structures from multivariate time-series data reveals important dependency relationships between current observations and histories, and provides a ...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only from the lack of class information but also the fact that high-dimensional data ...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Discrete mixed membership modeling and continuous latent factor modeling (also known as matrix factorization) are two popular, complementary approaches to dyadic data analysis. In...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
In a variety of applications, kernel machines such as Support Vector Machines (SVMs) have been used with great success often delivering stateof-the-art results. Using the kernel t...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We present a novel approach for structure prediction that addresses the difficulty of obtaining labeled structures for training. We observe that structured output problems often h...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
A new procedure for learning cost-sensitive SVM classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the cost-sensitive SVM is derived as the...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We consider the problem of learning a sparse multi-task regression, where the structure in the outputs can be represented as a tree with leaf nodes as outputs and internal nodes a...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Pseudo-likelihood and contrastive divergence are two well-known examples of contrastive methods. These algorithms trade off the probability of the correct label with the probabili...