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» On learning with dissimilarity functions
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ESANN
2001
13 years 10 months ago
Learning fault-tolerance in Radial Basis Function Networks
This paper describes a method of supervised learning based on forward selection branching. This method improves fault tolerance by means of combining information related to general...
Xavier Parra, Andreu Català
ML
2006
ACM
110views Machine Learning» more  ML 2006»
13 years 9 months ago
Classification-based objective functions
Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, ...
Michael Rimer, Tony Martinez
PKDD
2007
Springer
193views Data Mining» more  PKDD 2007»
14 years 3 months ago
Learning Multi-dimensional Functions: Gas Turbine Engine Modeling
Abstract. This paper shows how multi-dimensional functions, describing the operation of complex equipment, can be learned. The functions are points in a shape space, each produced ...
Chris Drummond
ICML
2005
IEEE
14 years 10 months ago
Proto-value functions: developmental reinforcement learning
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathematical model of a proto-value function: these are task-independent basis function...
Sridhar Mahadevan
AIR
2005
85views more  AIR 2005»
13 years 9 months ago
On Paradox of Fuzzy Modeling: Supervised Learning for Rectifying Fuzzy Membership Function
The paradox of fuzzy modeling is recognized due to the co-existence of its effectiveness of solving uncertain problems in the real world and the skepticism of its reasonability in ...
Shaopei Lin