A mixture of experts committee machine to design compensators for intensity modulated radiation therapy

J.H. Goodband, Olivier C.LK. Haas, John A. Mills

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

This paper presents a new algorithm to produce a near optimal mixture of experts model (MEM) architecture for a continuous mapping. The MEM is applied to a new method incorporating photon scatter for designing compensators for intensity modulated radiation therapy. The algorithm utilizes the fuzzy C-means clustering algorithm to partition data before training commences. A reduction in the size of training sets also allows the Levenberg–Marquardt algorithm to be implemented. As a result, both training time and validation error are reduced. A 71% reduction in prediction error compared with that of a single neural network is achieved.
Original languageEnglish
Pages (from-to)1704-1714
JournalPattern Recognition
Volume39
Issue number9
DOIs
Publication statusPublished - Sep 2006

Bibliographical note

This paper is not yet available on the repository

Funder

Engineering and Physical Sciences Research Council, Industrial Case Training award no. 02303507

Keywords

  • Committee machines
  • Neural networks
  • Fuzzy C-means
  • Compensators
  • Radiation therapy

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