Automatically assigned DDC number: 00632

Manually assigned DDC number: 00632

Number of references: 4

Title: Sample Complexity for Learning Recurrent Perceptron Mappings

Author:

Author:

Subject: Bhaskar Dasgupta,Eduardo D. Sontag Sample Complexity for Learning Recurrent Perceptron Mappings

Description: Recurrent perceptron classifiers generalize the classical perceptron model. They take into account those correlations and dependences among input coordinates which arise from linear digital filtering. This paper provides tight bounds on sample complexity associated to the fitting of such models to experimental data. 1 Introduction One of the most popular approaches to binary pattern classification, underlying many statistical techniques, is based on perceptrons or linear discriminants; see for instance the classical reference [9]. In this context, one is interested in classifying k-dimensional input patterns v = (v 1 ; : : : ; v k ) into two disjoint classes A + and A Gamma . A perceptron P which classifies vectors into A + and A Gamma is characterized by a vector (of "weights") ~c 2 R k , and operates as follows. One forms the inner product ~c:v = c 1 v 1 + : : : c k v k . If this inner product is positive, v is classified into A + , otherwise into A Gamma . (A va...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1995-06-19

Pubyear: 1995

Format: ps

Identifier: http://citeseer.ist.psu.edu/141462.html

Source: ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/1995/95-17.ps.gz

Language: en

Relation:

Relation:

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Rights: unrestricted

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