AN ADAPTIVELY TRAINED KERNEL-BASED NONLINEAR REPRESENTOR FOR HANDWRITTEN DIGIT CLASSIFICATION

被引：0
|
作者：
Liu Benyong Zhang Jing (School of Electronic Engineering
机构：
来源：
Journal of Electronics(China) | 2006年 / 03期
关键词：
Pattern recognition; Handwritten digit recognition; Incremental learning; Sparse representation; Kernel-based Nonlinear Representor (KNR);
D O I：
暂无
中图分类号：
TP391.43 [];
学科分类号：
0811 ; 081101 ; 081104 ; 1405 ;
摘要：
In practice, retraining a trained classifier is necessary when novel data become available. This paper adopts an incremental learning procedure to adaptively train a Kernel-based Nonlinear Representor (KNR), a recently presented nonlinear classifier for optimal pattern representation, so that its generalization ability may be evaluated in time-variant situation and a sparser representation is obtained for computationally intensive tasks. The addressed techniques are applied to handwritten digit classification to illustrate the feasibility for pattern recognition.
引用
收藏
页码：379 / 383
页数：5
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