Indefinite Support Vector Regression

被引：2
|
作者：
Schleif, Frank-Michael [1 ,2 ]
机构：
[1] Univ Appl Sci Wuerzburg Schweinfurt, D-97074 Wurzburg, Germany
[2] Univ Birmingham, Sch Comp Sci Edgbaston, Birmingham B15 2TT, W Midlands, England
来源：
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, PT II | 2017年 / 10614卷
关键词：
CLASSIFICATION; RECOGNITION; DISTANCE;
D O I：
10.1007/978-3-319-68612-7_36
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Non-metric proximity measures got wide interest in various domains such as life sciences, robotics and image processing. The majority of learning algorithms for these data are focusing on classification problems. Here we derive a regression algorithm for indefinite data representations based on the support vector machine. The approach avoids heuristic eigen spectrum modifications or costly proxy matrix approximations, as used in general. We evaluate the method on a number of benchmark data using an indefinite measure.
引用
收藏
页码：313 / 321
页数：9
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