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IEEEHPCS
2010
15 years 5 months ago
Discovering closed frequent itemsets on multicore: Parallelizing computations and optimizing memory accesses
The problem of closed frequent itemset discovery is a fundamental problem of data mining, having applications in numerous domains. It is thus very important to have efficient par...
Benjamin Négrevergne, Alexandre Termier, Je...
AAAI
2010
15 years 3 months ago
Multilinear Maximum Distance Embedding Via L1-Norm Optimization
Dimensionality reduction plays an important role in many machine learning and pattern recognition tasks. In this paper, we present a novel dimensionality reduction algorithm calle...
Yang Liu, Yan Liu, Keith C. C. Chan
LION
2009
Springer
129views Optimization» more  LION 2009»
16 years 1 months ago
Expeditive Extensions of Evolutionary Bayesian Probabilistic Neural Networks
Abstract. Probabilistic Neural Networks (PNNs) constitute a promising methodology for classification and prediction tasks. Their performance depends heavily on several factors, su...
Vasileios L. Georgiou, Sonia Malefaki, Konstantino...
ICML
2007
IEEE
16 years 7 months ago
Scalable training of L1-regularized log-linear models
The l-bfgs limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear models with L2 regularization, but it cannot be us...
Galen Andrew, Jianfeng Gao
GECCO
2009
Springer
126views Optimization» more  GECCO 2009»
15 years 11 months ago
Improving NSGA-II with an adaptive mutation operator
The performance of a Multiobjective Evolutionary Algorithm (MOEA) is crucially dependent on the parameter setting of the operators. The most desired control of such parameters pre...
Arthur Gonçalves Carvalho, Aluizio F. R. Ar...