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- W76693876 abstract "By using hidden nodes, Boltzmann machines can be employed to approximate probability distributions, store stochastic knowledge extracted from examples (a. k. a. learning) a nd retrieve the stored statistical information (a. k. a. inference or diagnosis). However, in ge neral, the algorithms for such operations are NP-hard. Efficient algorithms can therefore only be expected to be found in special cases. Such algorithms have recently been specified for a certain set of s o-called decimatable Boltzmann machines [9]. This approach is demonstrated for a small diagnosis system that was introduced in [5] as a didactic example: it deals with the joint probability of several medical variabl es. We show that a batch of fully diagnosed examples of medical data is sufficient not only to store the underlying stochastic correlations in a decimatable Boltzmann machine, but also to use this for inf erence or diagnosis. We argue that models like those obtained by decimatable Boltzmann machines — which learn from given examples only — may also help in building medically relevant models." @default.
- W76693876 created "2016-06-24" @default.
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- W76693876 date "1997-01-01" @default.
- W76693876 modified "2023-10-01" @default.
- W76693876 title "Decimatable Boltzmann Machines for Diagnosis: Efficient Learning and Inference" @default.
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