Efficient Optimization of Multi-class Support Vector Machines with MSVMpack

被引：2

作者：

Didiot, Emmanuel ^{[1
]}

Lauer, Fabien ^{[1
]}

机构：

[1] Univ Lorraine, CNRS, Inria, LORIA, Nancy, France

来源：

MODELLING, COMPUTATION AND OPTIMIZATION IN INFORMATION SYSTEMS AND MANAGEMENT SCIENCES - MCO 2015 - PT II | 2015年 / 360卷

关键词：

Quadratic programming; classification; support vector machines; parallel computing; SVM;

D O I：

10.1007/978-3-319-18167-7_3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the field of machine learning, multi-class support vector machines (M-SVMs) are state-of-the-art classifiers with training algorithms that amount to convex quadratic programs. However, solving these quadratic programs in practice is a complex task that typically cannot be assigned to a general purpose solver. The paper describes the main features of an efficient solver for M-SVMs, as implemented in the MSVMpack software. The latest additions to this software are also highlighted and a few numerical experiments are presented to assess its efficiency.

引用

页码：23 / 34

页数：12

共 50 条

[21] Hierarchical support vector machines for multi-class pattern recognition
Schwenker, Friedhelm
International Conference on Knowledge-Based Intelligent Electronic Systems, Proceedings, KES, 2000, 2 : 561 - 565
[22] BOOSTING ONE-CLASS SUPPORT VECTOR MACHINES FOR MULTI-CLASS CLASSIFICATION
Yeh, Chi-Yuan
Lee, Zhi-Ying
Lee, Shie-Jue
APPLIED ARTIFICIAL INTELLIGENCE, 2009, 23 (04) : 297 - 315
[23] Grey Wolf Optimization for One-Against-One Multi-class Support Vector Machines
Elhariri, Esraa
El-Bendary, Nashwa
Hassanien, Aboul Ella
Abraham, Ajith
PROCEEDINGS OF THE 2015 SEVENTH INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION (SOCPAR 2015), 2015, : 7 - 12
[24] Derivation, Optimization, and Comparative Analysis of Support Vector Machines Application to Multi-Class Image Data
Shekhar, Avi
Saeed, Amir K.
Johnson, Benjamin A.
Rodriguez, Benjamin M.
MULTIMODAL IMAGE EXPLOITATION AND LEARNING 2024, 2024, 13033
[25] Multi-class support vector machines for classification of transmission line faults
Ekici, Sami
ENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCH, 2012, 28 (02): : 1015 - 1026
[26] Support vector machines for multi-class signal classification with unbalanced samples
Xu, P
Chan, AK
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4, 2003, : 1116 - 1119
[27] Improved Binary Tree Support Vector Machines for Multi-class Classification
Pan, Yuqi
Zheng, Yanwei
2011 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND APPLICATIONS, 2011, : 111 - 116
[28] Tree-structured support vector machines for multi-class classification
Xia, Siyu
Li, Jiuxian
Xia, Liangzheng
Ju, Chunhua
ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 3, PROCEEDINGS, 2007, 4493 : 392 - +
[29] Fusing binary support vector machines (SVM) into multi-class SVM
Ying Zilu
Li Jingwen
Zhang Youwei
SIGNAL ANALYSIS, MEASUREMENT THEORY, PHOTO-ELECTRONIC TECHNOLOGY, AND ARTIFICIAL INTELLIGENCE, PTS 1 AND 2, 2006, 6357
[30] Disturbance Classification Utilizing Wavelet and Multi-class Support Vector Machines
Zang, Hongzhi
Yu, Xiaodong
ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 3, PROCEEDINGS, 2008, : 170 - +

← 1 2 3 4 5 →