ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
We present a comprehensive suite of experimentation on the subject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the pers...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
The scores returned by support vector machines are often used as a confidence measures in the classification of new examples. However, there is no theoretical argument sustaining ...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Many collective labeling tasks require inference on graphical models where the clique potentials depend only on the number of nodes that get a particular label. We design efficien...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically r...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Although each iteration of the popular kMeans clustering heuristic scales well to larger problem sizes, it often requires an unacceptably-high number of iterations to converge to ...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
We1 present a new actor-critic learning model in which a Bayesian class of non-parametric critics, using Gaussian process temporal difference learning is used. Such critics model ...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
The UCT algorithm learns a value function online using sample-based search. The TD() algorithm can learn a value function offline for the on-policy distribution. We consider three...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Intuitively, learning should be easier when the data points lie on a low-dimensional submanifold of the input space. Recently there has been a growing interest in algorithms that ...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
In this work we take a novel view of nonlinear manifold learning. Usually, manifold learning is formulated in terms of finding an embedding or `unrolling' of a manifold into ...