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» Learning with Neural Networks in the Domain of Graphs
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PPSN
1990
Springer
15 years 1 months ago
Feature Construction for Back-Propagation
T h e ease of learning concepts f r o m examples in empirical machine learning depends on the attributes used for describing the training d a t a . We show t h a t decision-tree b...
Selwyn Piramuthu
GECCO
2010
Springer
184views Optimization» more  GECCO 2010»
15 years 2 months ago
Transfer learning through indirect encoding
An important goal for the generative and developmental systems (GDS) community is to show that GDS approaches can compete with more mainstream approaches in machine learning (ML)....
Phillip Verbancsics, Kenneth O. Stanley
IJCINI
2007
125views more  IJCINI 2007»
14 years 9 months ago
A Unified Approach To Fractal Dimensions
The Cognitive Processes of Abstraction and Formal Inferences J. A. Anderson: A Brain-Like Computer for Cognitive Software Applications: the Resatz Brain Project L. Flax: Cognitive ...
Witold Kinsner
LWA
2004
14 years 11 months ago
Modeling Rule Precision
This paper reports first results of an empirical study of the precision of classification rules on an independent test set. We generated a large number of rules using a general co...
Johannes Fürnkranz
JMLR
2010
162views more  JMLR 2010»
14 years 4 months ago
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging ...
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom D...