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» LEO - DB2's LEarning Optimizer
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188
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ACML
2009
Springer
16 years 20 days ago
Linear Time Model Selection for Mixture of Heterogeneous Components
Abstract: Our main contribution is to propose a novel model selection methodology, expectation minimization of information criterion (EMIC). EMIC makes a significant impact on the...
Ryohei Fujimaki, Satoshi Morinaga, Michinari Momma...
ICASSP
2008
IEEE
16 years 15 days ago
Contextually adaptive signal representation using conditional principal component analysis
The conventional method of generating a basis that is optimally adapted (in MSE) for representation of an ensemble of signals is Principal Component Analysis (PCA). A more ambitio...
Rosa M. Figueras i Ventura, Umesh Rajashekar, Zhou...
COLT
2007
Springer
16 years 7 days ago
Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking
We describe and analyze a new approach for feature ranking in the presence of categorical features with a large number of possible values. It is shown that popular ranking criteria...
Sivan Sabato, Shai Shalev-Shwartz
153
Voted
ECML
2007
Springer
16 years 7 days ago
Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks
Abstract. The paper investigates modification of backpropagation algorithm, consisting of discretization of neural network weights after each training cycle. This modification, a...
Marcin Wojnarski
GECCO
2010
Springer
200views Optimization» more  GECCO 2010»
15 years 11 months ago
Multivariate multi-model approach for globally multimodal problems
This paper proposes an estimation of distribution algorithm (EDA) aiming at addressing globally multimodal problems, i.e., problems that present several global optima. It can be r...
Chung-Yao Chuang, Wen-Lian Hsu