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» Approximate algorithms for neural-Bayesian approaches
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COMGEO
2006
ACM
14 years 9 months ago
A new algorithmic approach to the computation of Minkowski functionals of polyconvex sets
An algorithm is proposed for the simultaneous computation of all Minkowski functionals (except for the volume) of sets from the convex ring in Rd discretized with respect to a give...
Simone Klenk, Volker Schmidt, Evgueni Spodarev
EDBT
2008
ACM
103views Database» more  EDBT 2008»
15 years 9 months ago
A stratified approach to progressive approximate joins
Users often do not require a complete answer to their query but rather only a sample. They expect the sample to be either the largest possible or the most representative (or both)...
Wee Hyong Tok, Stéphane Bressan, Mong-Li Le...
NIPS
1998
14 years 11 months ago
Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models
We present Monte-Carlo generalized EM equations for learning in nonlinear state space models. The dif
Thomas Briegel, Volker Tresp
FCT
2007
Springer
15 years 3 months ago
Analysis of Approximation Algorithms for k-Set Cover Using Factor-Revealing Linear Programs
We present new combinatorial approximation algorithms for k-set cover. Previous approaches are based on extending the greedy algorithm by efficiently handling small sets. The new a...
Stavros Athanassopoulos, Ioannis Caragiannis, Chri...
KDD
2004
ACM
158views Data Mining» more  KDD 2004»
15 years 10 months ago
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an informationtheoretic ...
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Gho...