Adaptive mixtures of local experts

Authors:
Robert A. Jacobs;Michael I. Jordan;Steven J. Nowlan;Geoffrey E. Hinton
Affiliations:
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA;Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA;Department of Computer Science, University of Toronto, Toronto, Canada M5S 1A4;Department of Computer Science, University of Toronto, Toronto, Canada M5S 1A4
Venue:
Neural Computation
Year:
1991

Citing 1
Cited 309

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Localized algorithms for multiple kernel learning

Pattern Recognition
How important are "Deformable parts" in the deformable parts model?

ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

International Journal of Automation and Computing
A model-learner pattern for bayesian reasoning

POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Investigation of mixture of experts applied to residential premises valuation

ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Behavioral repertoire learning in robotics

Proceedings of the 15th annual conference on Genetic and evolutionary computation
Don't be SCAREd: use SCalable Automatic REpairing with maximal likelihood and bounded changes

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A framework for merging and ranking of answers in DeepQA

IBM Journal of Research and Development
Intelligent systems developed for the early detection of chronic kidney disease

Advances in Artificial Neural Systems
Learning to select and generalize striking movements in robot table tennis

International Journal of Robotics Research
Learning from multiple annotators: Distinguishing good from random labelers

Pattern Recognition Letters
Minimum distance estimation in a finite mixture regression model

Journal of Multivariate Analysis
Learning symbolic representations of hybrid dynamical systems

The Journal of Machine Learning Research
New cluster ensemble approach to integrative biological data analysis

International Journal of Data Mining and Bioinformatics
Multi-modal convergence maps: from body schema and self-representation to mental imagery

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Nonnegative Least-Squares Methods for the Classification of High-Dimensional Biological Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Classification model for detecting and managing credit loan fraud based on individual-level utility concept

ACM SIGMIS Database
Online learning of single-and multivalued functions with an infinite mixture of linear experts

Neural Computation
Combining classifiers using nearest decision prototypes

Applied Soft Computing
A survey of multiple classifier systems as hybrid systems

Information Fusion
Mixtures of experts for understanding model discrepancy in dynamic computer models

Computational Statistics & Data Analysis
Meta-ELM: ELM with ELM hidden nodes

Neurocomputing
Neural network modeling of vector multivariable functions in ill-posed approximation problems

Journal of Computer and Systems Sciences International
Boosted Pre-loaded Mixture of Experts for low-resolution face recognition

International Journal of Hybrid Intelligent Systems
Adaptive fuzzy system to forecast financial time series volatility

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Robust human action recognition scheme based on high-level feature fusion

Multimedia Tools and Applications
Embedded local feature selection within mixture of experts

Information Sciences: an International Journal

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Abstract

We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network.