Titlebook: An Information-Theoretic Approach to Neural Computing; Gustavo Deco,Dragan Obradovic Book 1996 Springer-Verlag New York, Inc. 1996 calculu

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書目名稱An Information-Theoretic Approach to Neural Computing讀者反饋




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Confess

https://doi.org/10.1007/978-3-642-92788-1asily applied are stochastic Boolean networks, i.e. Boltzmann Machines, which are the main topic of this chapter. The simplicity is due to the fact that the outputs of the network units are binary (and therefore very limited) and that the output probabilities can be explicitly calculated.

FLAIL

Die Prognose der Essentiellen Hypertoniering learning or by appropriately modifying the cost function. Akaike’s and Rissanen’s methods for formulating cost functions which naturally include model complexity terms are presented in Chapter 7 while the problem of generalization over an infinite ensemble of networks is presented in Chapter 8.

冒號(hào)

Nonlinear Feature Extraction: Boolean Stochastic Networksasily applied are stochastic Boolean networks, i.e. Boltzmann Machines, which are the main topic of this chapter. The simplicity is due to the fact that the outputs of the network units are binary (and therefore very limited) and that the output probabilities can be explicitly calculated.

機(jī)制

Information Theory Based Regularizing Methodsring learning or by appropriately modifying the cost function. Akaike’s and Rissanen’s methods for formulating cost functions which naturally include model complexity terms are presented in Chapter 7 while the problem of generalization over an infinite ensemble of networks is presented in Chapter 8.

權(quán)宜之計(jì)

Book 1996rks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this to be a very valuable introduction to this topic.

小故事

Book 1996mulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular they show how these methods may be applied to the topics of supervised and unsupervised learning including feature extra

CHOKE

Linear Feature Extraction: Infomax Principleenables processing of the higher order cognitive functions. Chapter 3 and Chapter 4 focus on the case of linear feature extraction. Linear feature extraction removes redundancy from the data in a linear fashion.

制造

Konstitutioneller h?molytischer IkterusThis chapter presents a brief overview of the principal concepts and fundaments of information theory and the theory of neural networks.

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