A training algorithm for classification of high-dimensional data

被引：16

作者：

Vieira, A

Barradas, N

机构：

[1] ISEP, Dept Fis, P-4200 Oporto, Portugal

[2] Univ Coimbra, Ctr Fis Computac, P-3000 Coimbra, Portugal

[3] Inst Tecnol & Nucl, P-2686953 Sacavem, Portugal

[4] Univ Lisbon, Ctr Fis Nucl, P-1699 Lisbon, Portugal

来源：

NEUROCOMPUTING | 2003年 / 50卷

关键词：

classification; learning vector quantization; hidden layer leaming vector quantization; feature extraction; Rutherford backscattering;

D O I：

10.1016/S0925-2312(02)00635-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose an algorithm for training multi layer preceptrons (MLP) for classification problems, that we named hidden layer learning vector quantization. It consists of applying learning vector quantization to the last hidden layer of a MLP and it gave very successful results on problems containing a large number of correlated inputs. It was applied with excellent results on classification of Rutherford backscattering spectra and to a benchmark problem of image recognition. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：461 / 472

页数：12

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