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» Adapting the Fitness Function in GP for Data Mining
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NIPS
2003
13 years 8 months ago
Nonstationary Covariance Functions for Gaussian Process Regression
We introduce a class of nonstationary covariance functions for Gaussian process (GP) regression. Nonstationary covariance functions allow the model to adapt to functions whose smo...
Christopher J. Paciorek, Mark J. Schervish
ICDM
2006
IEEE
158views Data Mining» more  ICDM 2006»
14 years 1 months ago
A Probability Distribution Of Functional Random Variable With A Functional Data Analysis Application
Probability distributions are central tools for probabilistic modeling in data mining, and they lack in functional data analysis (FDA). In this paper we propose a probability dist...
Etienne Cuvelier, Monique Noirhomme-Fraiture
EPIA
2003
Springer
14 years 22 days ago
Evolutionary Neuroestimation of Fitness Functions
Abstract. One of the most influential factors in the quality of the solutions found by an evolutionary algorithm is the appropriateness of the fitness function. Specifically in ...
Jesús S. Aguilar-Ruiz, Daniel Mateos, Domin...
CGF
2002
142views more  CGF 2002»
13 years 7 months ago
Efficient Fitting and Rendering of Large Scattered Data Sets Using Subdivision Surfaces
We present a method to efficiently construct and render a smooth surface for approximation of large functional scattered data. Using a subdivision surface framework and techniques...
Vincent Scheib, Jörg Haber, Ming C. Lin, Hans...
TEC
2008
104views more  TEC 2008»
13 years 7 months ago
Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy
Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data i...
Tzung-Pei Hong, Chun-Hao Chen, Yeong-Chyi Lee, Yu-...