ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
We present an efficient method for maximizing energy functions with first and second order potentials, suitable for MAP labeling estimation problems that arise in undirected graph...
ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a nov...
ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
If appropriately used, prior knowledge can significantly improve the predictive accuracy of learning algorithms or reduce the amount of training data needed. In this paper we intr...
ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
The Gaussian process latent variable model (GP-LVM) is a generative approach to nonlinear low dimensional embedding, that provides a smooth probabilistic mapping from latent to da...
ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that est...
ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
Kernel learning plays an important role in many machine learning tasks. However, algorithms for learning a kernel matrix often scale poorly, with running times that are cubic in t...
ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
A multiclass classification problem can be reduced to a collection of binary problems with the aid of a coding matrix. The quality of the final solution, which is an ensemble of b...
ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
We propose efficient particle smoothing methods for generalized state-spaces models. Particle smoothing is an expensive O(N2 ) algorithm, where N is the number of particles. We ov...
ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
We consider the problem of choosing a linear classifier that minimizes misclassification probabilities in two-class classification, which is a bi-criterion problem, involving a tr...
ICML
2006
IEEE
15 years 7 days ago
2006
IEEE
In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance...