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PDCN
2004
14 years 11 months ago
K-Means VQ algorithm using a low-cost parallel cluster computing
It is well-known that the time and memory necessary to create a codebook from large training databases have hindered the vector quantization based systems for real applications. T...
Paulo Sergio Lopes de Souza, Alceu de Souza Britto...
78
Voted
PPSN
2010
Springer
14 years 8 months ago
General Lower Bounds for the Running Time of Evolutionary Algorithms
Abstract. We present a new method for proving lower bounds in evolutionary computation based on fitness-level arguments and an additional condition on transition probabilities bet...
Dirk Sudholt
73
Voted
ICDAR
2003
IEEE
15 years 2 months ago
A Low-Cost Parallel K-Means VQ Algorithm Using Cluster Computing
In this paper we propose a parallel approach for the Kmeans Vector Quantization (VQ) algorithm used in a twostage Hidden Markov Model (HMM)-based system for recognizing handwritte...
Alceu de Souza Britto Jr., Paulo Sergio Lopes de S...
96
Voted
GECCO
2008
Springer
163views Optimization» more  GECCO 2008»
14 years 10 months ago
Embedded evolutionary multi-objective optimization for worst case robustness
In Multi-Objective Problems (MOPs) involving uncertainty, each solution might be associated with a cluster of performances in the objective space depending on the possible scenari...
Gideon Avigad, Jürgen Branke
GECCO
2007
Springer
200views Optimization» more  GECCO 2007»
15 years 3 months ago
Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms
Recent research into single–objective continuous Estimation– of–Distribution Algorithms (EDAs) has shown that when maximum–likelihood estimations are used for parametric d...
Peter A. N. Bosman, Dirk Thierens