Object

Title: On a Classification Method for a Large Number of Classes

Co-author(s) :

Ryazanov Valery ; Aslanyan Levon ; Sahakyan Hasmik

Abstract:

The construction of a two-level decision scheme for recognition problems with many classes is proposed that is based on the development of the error-correcting output codes (ЕСОС) method. In the “classical” ЕСОС, a large number of partitions of the original classes into two macroclasses are constructed. Each macroclass is a union of some original classes. Each macroclass is assigned either 0 or 1. As a result, each original class is defined by a row of 0 and 1 (the stage of encoding) and a coding matrix is constructed. The stage of classification of an arbitrary new object consists in the solution of each dichotomic problem and application of a special decision rule (the stage of decoding). In this paper, new methods for weighting and taking into account codewords, modifying decision rules, and searching for locally optimal dichotomies are proposed, and various quality criteria for classification and the cases of extension of a codeword are considered.

Publisher:

Springer

Date accepted:

27.01.2019

Date of publication:

18.09.2019

Identifier:

oai:noad.sci.am:136159

DOI:

10.1134/S1054661819030246

ISSN:

1054-6618

Language:

English

Journal or Publication Title:

Pattern Recognition and Image Analysis volume

Volume:

29

Number:

3

URL:


Affiliation:

Dorodnitsyn Computing Center, Federal Research Center Informatics and Control, Russian Academy of Sciences ; Moscow Institute of Physics and Technology (State University) ; Institute for Informatics and Automation Problems

Country:

Armenia ; Russia

Indexing:

Scopus

Object collections:

Last modified:

Apr 19, 2021

In our library since:

Apr 19, 2021

Number of object content hits:

26

All available object's versions:

https://noad.sci.am/publication/149407

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