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STOC
1993
ACM
141views Algorithms» more  STOC 1993»
15 years 11 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
HAIS
2009
Springer
15 years 11 months ago
Pareto-Based Multi-output Model Type Selection
In engineering design the use of approximation models (= surrogate models) has become standard practice for design space exploration, sensitivity analysis, visualization and optimi...
Dirk Gorissen, Ivo Couckuyt, Karel Crombecq, Tom D...

Book
498views
17 years 5 months ago
Machine Learning, Neural and Statistical Classification
This book covers several topics such as Classification, Classical Statistical Methods, Modern Statistical Techniques, Machine Learning of Rules and Trees, Neural Networks Methods ...
Ellis Horwood
IJCNN
2007
IEEE
16 years 1 months ago
Evolving a Neural Net-Based Decision and Search Heuristic for DPLL SAT Solvers
— Solvers for the Boolean satisfiability problem are an important base technology for many applications. The most efficient SAT solvers for industrial applications are based on...
Raihan H. Kibria
ICONIP
2004
15 years 8 months ago
Neural-Evolutionary Learning in a Bounded Rationality Scenario
Abstract. This paper presents a neural-evolutionary framework for the simulation of market models in a bounded rationality scenario. Each agent involved in the scenario make use of...
Ricardo Matsumura de Araújo, Luís C....