Title :
Cross-entropy based pruning of the hierarchical mixtures of experts
Author :
Whitworth, C.C. ; Kadirkamanathan, V.
Author_Institution :
Dept. of Autom. Control & Syst. Eng., Sheffield Univ., UK
Abstract :
The paper presents a pruning scheme for the hierarchical mixtures of experts (HME), which is a hierarchical and tree-like modular neural network trained using the EM-algorithm. The pruning scheme is in the style of the classification and regression tree (CART), and consists of using cross-entropy to select and cut out sub-trees of the HME to create a series of nested HMEs. The right sized HME can then be selected by using cross-validation. Experiments are carried out to demonstrate the successful operation of the scheme
Keywords :
divide and conquer methods; entropy; neural nets; pattern classification; problem solving; statistical analysis; trees (mathematics); CART; classification tree; cross-entropy based pruning; cross-validation; hierarchical expert mixtures; hierarchical tree-like modular neural network; nested HME; pruning scheme; regression tree; Classification tree analysis; Logistics; Merging; Neural networks; Regression tree analysis; Systems engineering and theory;
Conference_Titel :
Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
Conference_Location :
Amelia Island, FL
Print_ISBN :
0-7803-4256-9
DOI :
10.1109/NNSP.1997.622418
