Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection

被引：3

作者：

Villmann, Thomas ^{[1
]}

Kaden, Marika ^{[1
]}

Nebel, David ^{[1
]}

Biehl, Michael ^{[2
]}

机构：

[1] Univ Appl Sci Mittweida, Computat Intelligence Grp, Mittweida, Germany

[2] Univ Groningen, Johann Bernoulli Inst Math & Comp Sci, NL-9700 AK Groningen, Netherlands

来源：

COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2015, PT II | 2015年 / 9257卷

关键词：

CLASSIFICATION;

D O I：

10.1007/978-3-319-23117-4_66

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider a reject option for prototype-based Learning Vector Quantization (LVQ), which facilitates the detection of outliers in the data during the classification process. The rejection mechanism is based on a distance-based criterion and the corresponding threshold is automatically adjusted in the training phase according to pre-defined rejection costs. The adaptation of LVQ prototypes is simultaneously guided by the complementary aims of low classification error, faithful representation of the observed data, and low total rejection costs.

引用

页码：772 / 782

页数：11

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