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International Journal of Pattern Recognition and Artificial Intelligence
 

Summary: International Journal of Pattern Recognition
and Artificial Intelligence
Vol. 23, No. 2 (2009) 159­190
c World Scientific Publishing Company
AN INCREMENTAL FRAMEWORK BASED
ON CROSS-VALIDATION FOR ESTIMATING THE
ARCHITECTURE OF A MULTILAYER PERCEPTRON
OYA ARAN, OLCAY TANER YILDIZ
and ETHEM ALPAYDIN
Department of Computer Engineering, Bogazi¸ci University
TR-34342, Istanbul, Turkey
aranoya@boun.edu.tr
olcaytaner@isikuniv.edu.tr
alpaydin@boun.edu.tr
We define the problem of optimizing the architecture of a multilayer perceptron (MLP) as
a state space search and propose the MOST (Multiple Operators using Statistical Tests)
framework that incrementally modifies the structure and checks for improvement using
cross-validation. We consider five variants that implement forward/backward search,
using single/multiple operators and searching depth-first/breadth-first. On 44 classifi-
cation and 30 regression datasets, we exhaustively search for the optimal and evaluate

  

Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University

 

Collections: Computer Technologies and Information Sciences