Automatically assigned DDC number: 00632

Manually assigned DDC number: 006312

Number of references: 4

Title: Pattern Recognition and Neural Networks

Author:

Author:

Subject: Yoshua Bengio,Yann Lecun Pattern Recognition and Neural Networks

Description: INTRODUCTION Pattern Recognition (PR) addresses the problem of classifying objects, often represented as vectors or as strings of symbols, into categories. The difficulty is to synthesize, and then to efficiently compute, the classification function that maps objects to categories, given that objects in a category can have widely varying input representations. In most instances, the task is known to the designer through a set of example patterns whose categories are known, and through general, a priori knowledge about the task, such as: "the category of an object is not changed when the object is slightly translated or rotated in space". Historically, the field of PR started with the early efforts in Neural Networks (Perceptrons, Adalines...). While in the past, NNs have somewhat played the role of an outsider in PR, the recent progress in learning algorithms (and the availability of powerful hardware) have made them the method of choice for many PR applications

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1998-07-18

Pubyear: 1995

Format: ps

Identifier: http://citeseer.ist.psu.edu/148271.html

Source: http://www.research.att.com/~yann/publis/./psgz/lecun-bengio-95b.ps.gz

Language: en

Relation:

Relation:

Relation:

Relation:

Rights: unrestricted

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