509 search results - page 34 / 102 » Using Learning for Approximation in Stochastic Processes |
120
Voted
ICML
2009
IEEE
16 years 3 months ago
2009
IEEE
The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian proces...
126
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ICML
2010
IEEE
15 years 3 months ago
2010
IEEE
In Sparse Coding (SC), input vectors are reconstructed using a sparse linear combination of basis vectors. SC has become a popular method for extracting features from data. For a ...
100
Voted
BC
2006
15 years 2 months ago
2006
Recruitment learning in hierarchies is an inherently unstable process (Valiant, 1994). This paper presents conditions on parameters for a feedforward network to ensure stable recru...
137
Voted
CORR
2008
Springer
15 years 2 months ago
2008
Springer
This paper presents improved approximation algorithms for the problem of multiprocessor scheduling under uncertainty (SUU), in which the execution of each job may fail probabilist...
115
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NIPS
2004
15 years 3 months ago
2004
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to appro...