A New Learning Method for Piecewise Linear Regression

A new connectionist model for the solution of piecewise lin- ear regression problems is introduced; it is able to reconstruct both con- tinuous and non continuous real valued mappings starting from a finite set of possibly noisy samples. The approximating function can assume a different linear behavior in each region of an unknown polyhedral parti- tion of the input domain. The proposed learning technique combines local estimation, clustering in weight space, multicategory classification and linear regression in order to achieve the desired result. Through this approach piecewise affine solutions for general nonlinear regression problems can also be found.


Published in:
Artificial Neural Networks - ICANN 2002: International Conference, Madrid, Spain, August 28-30, 2002. Proceedings. Lecture Notes in Computer Science, 2623, 444-449
Year:
2002
Publisher:
Springer-Verlag, Heidelberg
ISBN:
978-3-540-44074-1
Note:
Madrid, Spain
Laboratories:




 Record created 2017-01-10, last modified 2018-09-13


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