ISSN:
1573-7497
Keywords:
Neural Networks
;
Boltzmann Machines
;
High Order networks
;
Classification Problems
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract This work reports the results obtained with the application of High Order Boltzmann Machines without hidden units to construct classifiers for some problems that represent different learning paradigms. The Boltzmann Machine weight updating algorithm remains the same even when some of the units can take values in a discrete set or in a continuous interval. The absence of hidden units and the restriction to classification problems allows for the estimation of the connection statistics, without the computational cost involved in the application of simulated annealing. In this setting, the learning process can be sped up several orders of magnitude with no appreciable loss of quality of the results obtained.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1008257203142
Permalink
