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» Local and Global Complete Solution Learning Methods for QBF
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ICML
2008
IEEE
14 years 6 months ago
Estimating local optimums in EM algorithm over Gaussian mixture model
EM algorithm is a very popular iteration-based method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is no...
Zhenjie Zhang, Bing Tian Dai, Anthony K. H. Tung
AAAI
1994
13 years 6 months ago
Solution Reuse in Dynamic Constraint Satisfaction Problems
Many AI problems can be modeled as constraint satisfaction problems (CSP), but many of them are actually dynamic: the set of constraints to consider evolves because of the environ...
Gérard Verfaillie, Thomas Schiex
TASLP
2010
138views more  TASLP 2010»
12 years 12 months ago
Glimpsing IVA: A Framework for Overcomplete/Complete/Undercomplete Convolutive Source Separation
Abstract--Independent vector analysis (IVA) is a method for separating convolutedly mixed signals that significantly reduces the occurrence of the well-known permutation problem in...
Alireza Masnadi-Shirazi, Wenyi Zhang, Bhaskar D. R...
GMP
2002
IEEE
164views Solid Modeling» more  GMP 2002»
13 years 10 months ago
Construct Piecewise Hermite Interpolation Surface with Blending Methods
Three methods are proposed to construct a piecewise Hermite interpolation surface (PHIS), which is a piecewise algebraic surface interpolating a set of given points with associated...
Xiao-Shan Gao, Ming Li
AAAI
1998
13 years 6 months ago
Applying Online Search Techniques to Continuous-State Reinforcement Learning
In this paper, we describe methods for e ciently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boo...
Scott Davies, Andrew Y. Ng, Andrew W. Moore