Name
Abstract | ||
|---|---|---|
The Boltzmann machine is a probabilistic neural network describing the associative dependency of variables. It yields a probability distribution, which is a special case of the distribution generated by probabilistic inference networks. Hence both types of networks can be combined allowing to integrate probabilistic rules as well as unspecified associations in a sound way. The resulting network may have a number of interesting features including cycles of probabilistic rules, and hidden "unobservable" variables. The maximum likelihood approach is used to combine possibly conflicting pieces of information on rules or associations. |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1002/int.4550070107 | INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS |
Keywords | Field | DocType |
probabilistic neural network,probabilistic reasoning,probability distribution,boltzmann machine | Divergence-from-randomness model,Probabilistic CTL,Probabilistic analysis of algorithms,Probabilistic neural network,Artificial intelligence,Probabilistic argumentation,Probabilistic logic,Probabilistic relevance model,Mathematics,Machine learning,Probabilistic database | Journal |
Volume | Issue | ISSN |
7 | 1 | 0884-8173 |
Citations | PageRank | References |
3 | 0.49 | 1 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gerhard Paass | 1 | 1136 | 83.63 |