Accession Number:

ADA276516

Title:

Hierarchical Mixtures of Experts and the EM Algorithm

Descriptive Note:

Memorandum rept.

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Report Date:

1993-08-06

Pagination or Media Count:

31.0

Abstract:

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models GLIMs. Learning is treated as a maximum likelihood problem in particular, we present an Expectation-Maximization EM algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

Subject Categories:

  • Statistics and Probability
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE