509 search results - page 55 / 102 » Using Learning for Approximation in Stochastic Processes |
102
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PAMI
2006
15 years 2 months ago
2006
Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
96
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NIPS
2007
15 years 3 months ago
2007
In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a shared covariance function on input-dependent features...
ICANN
2007
Springer
15 years 8 months ago
2007
Springer
Abstract. This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov...
135
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CISS
2007
IEEE
15 years 6 months ago
2007
IEEE
Abstract-Joint subcarrier, power and rate allocation in orthogonal frequency division multiple access (OFDMA) scheduling is investigated for both downlink and uplink wireless trans...
137
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GECCO
2009
Springer
15 years 7 months ago
2009
Springer
We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are c...