Hierarchical Mixtures of Experts and the EM Algorithm

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 (GLIM's). 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.

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Document Details

Document Type
Technical Report
Publication Date
Aug 06, 1993
Accession Number
ADA276516

Entities

People

  • Michael I. Jordan
  • Robert A. Jacobs

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Counter IED

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Cognitive Science
  • Computational Science
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Network Science
  • Neural Networks
  • Probability
  • Random Variables
  • Statistical Analysis
  • Statistics
  • Supervised Machine Learning
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy