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AAAI
1998
15 years 8 months ago
Optimal 2D Model Matching Using a Messy Genetic Algorithm
A Messy Genetic Algorithm is customized toflnd'optimal many-to-many matches for 2D line segment models. The Messy GA is a variant upon the Standard Genetic Algorithm in which...
J. Ross Beveridge
JGAA
2007
142views more  JGAA 2007»
15 years 6 months ago
Approximation Algorithms for the Maximum Induced Planar and Outerplanar Subgraph Problems
The task of finding the largest subset of vertices of a graph that induces a planar subgraph is known as the Maximum Induced Planar Subgraph problem (MIPS). In this paper, some n...
Kerri Morgan, Graham Farr
JMLR
2010
111views more  JMLR 2010»
15 years 1 months ago
An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models
Logic-based probabilistic models (LBPMs) enable us to handle problems with uncertainty succinctly thanks to the expressive power of logic. However, most of LBPMs have restrictions...
Masakazu Ishihata, Yoshitaka Kameya, Taisuke Sato,...
ICIP
2001
IEEE
16 years 8 months ago
EM algorithms of Gaussian mixture model and hidden Markov model
The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Mo...
Guorong Xuan, Wei Zhang, Peiqi Chai
STOC
2007
ACM
106views Algorithms» more  STOC 2007»
16 years 7 months ago
One sketch for all: fast algorithms for compressed sensing
Compressed Sensing is a new paradigm for acquiring the compressible signals that arise in many applications. These signals can be approximated using an amount of information much ...
Anna C. Gilbert, Martin J. Strauss, Joel A. Tropp,...