JMLR
2010
13 years 7 months ago
2010
Different aspects of the curse of dimensionality are known to present serious challenges to various machine-learning methods and tasks. This paper explores a new aspect of the dim...
JMLR
2010
13 years 7 months ago
2010
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
JMLR
2010
13 years 7 months ago
2010
This paper presents a statistical model for expressing preferences through rankings, when the number of alternatives (items to rank) is large. A human ranker will then typically r...
JMLR
2010
13 years 7 months ago
2010
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
JMLR
2010
13 years 7 months ago
2010
This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algori...
JMLR
2010
13 years 7 months ago
2010
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library ...
JMLR
2010
13 years 7 months ago
2010
Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact r...
JMLR
2010
13 years 7 months ago
2010
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
JMLR
2010
13 years 7 months ago
2010
This paper considers the problem of estimating a high dimensional inverse covariance matrix that can be well approximated by "sparse" matrices. Taking advantage of the c...
JMLR
2010
13 years 7 months ago
2010
This paper develops algorithms to train support vector machines when training data are distributed across different nodes, and their communication to a centralized processing unit...