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
相关论文
共 50 条
  • [41] Analysis of Support Vector Machines Regression
    Tong, Hongzhi
    Chen, Di-Rong
    Peng, Lizhong
    FOUNDATIONS OF COMPUTATIONAL MATHEMATICS, 2009, 9 (02) : 243 - 257
  • [42] Application of Support Vector Regression in Beamforming
    Cui Lin
    Li Yaan
    Li Xiaohua
    Liu Wangsheng
    2011 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), VOLS 1-4, 2012, : 1270 - 1273
  • [43] An overview on twin support vector regression
    Huang, Huajuan
    Wei, Xiuxi
    Zhou, Yongquan
    NEUROCOMPUTING, 2022, 490 : 80 - 92
  • [44] Incremental learning for ν-Support Vector Regression
    Gu, Bin
    Sheng, Victor S.
    Wang, Zhijie
    Ho, Derek
    Osman, Said
    Li, Shuo
    NEURAL NETWORKS, 2015, 67 : 140 - 150
  • [45] Support vector regression with smoothing property
    Yang, ZX
    Wang, N
    Jing, L
    ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS, 2005, 3610 : 217 - 220
  • [46] Asymmetric ν-tube support vector regression
    Huang, Xiaolin
    Shi, Lei
    Pelckmans, Kristiaan
    Suykens, Johan A. K.
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2014, 77 : 371 - 382
  • [47] A geometric approach to support vector regression
    Bi, JB
    Bennett, KP
    NEUROCOMPUTING, 2003, 55 (1-2) : 79 - 108
  • [48] Support vector regression for surveillance purposes
    Ozer, Sedat
    Cirpan, Hakan A.
    Kabaoglu, Nihat
    MULTIMEDIA CONTENT REPRESENTATION, CLASSIFICATION AND SECURITY, 2006, 4105 : 442 - 449
  • [49] Square penalty support vector regression
    Barbero, Alvaro
    Lopez, Jorge
    Dorronsoro, Jose R.
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007, 2007, 4881 : 537 - 546
  • [50] ε-Distance Weighted Support Vector Regression
    Ou, Ge
    Wang, Yan
    Huang, Lan
    Pang, Wei
    Coghill, George Macleod
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2018, PT I, 2018, 10937 : 209 - 220
12345