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

fYear

1997

fDate

24-26 Sep 1997

Firstpage

375

Lastpage

383

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop

Conference_Location

Amelia Island, FL

ISSN

1089-3555

Print_ISBN

0-7803-4256-9

Type

conf

DOI

10.1109/NNSP.1997.622418

Filename

622418

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2400411