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Artificial neural networks and their...

Bengio, Yoshua.

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Artificial neural networks and their application to sequence recognition.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial neural networks and their application to sequence recognition./
作者:	Bengio, Yoshua.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 1991,
面頁冊數:	180 p.
附註:	Source: Dissertation Abstracts International, Volume: 53-12, Section: B, page: 6387.
Contained By:	Dissertation Abstracts International53-12B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NN72116
ISBN:	9780315721166

Artificial neural networks and their application to sequence recognition.
Bengio, Yoshua.

Artificial neural networks and their application to sequence recognition. - Ann Arbor : ProQuest Dissertations & Theses, 1991 - 180 p.

Source: Dissertation Abstracts International, Volume: 53-12, Section: B, page: 6387.

Thesis (Ph.D.)--McGill University (Canada), 1991.

This thesis studies the introduction of a priori structure into the design of learning systems based on artificial neural networks applied to sequence recognition, in particular to phoneme recognition in continuous speech. Because we are interested in sequence analysis, algorithms for training recurrent networks are studied and an original algorithm for constrained recurrent networks is proposed and test results are reported. We also discuss the integration of connectionist models with other analysis tools that have been shown to be useful for sequences, such as dynamic programming and hidden Markov models. We introduce an original algorithm to perform global optimization of a neural network/hidden Markov model hybrid, and show how to perform such a global optimization on all the parameters of the system. Finally, we consider some alternatives to sigmoid networks: Radial Basis Functions, and a method for searching for better learning rules using a priori knowledge and optimization algorithms.

ISBN: 9780315721166Subjects--Topical Terms:

523869
Computer science.

Artificial neural networks and their application to sequence recognition.
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