Relevance regression learning with support vector machines

被引：6

作者：

Apolloni, Bruno ^{[1
]}

Malchiodi, Dario ^{[1
]}

Valerio, Lorenzo ^{[2
]}

机构：

[1] Univ Milan, Dip Sci Informaz, I-20135 Milan, Italy

[2] Univ Milan, Dip Matemat Federigo Enriques, I-20133 Milan, Italy

来源：

NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS | 2010年 / 73卷 / 09期

关键词：

SVM; Regression; Uncertainty management; Relevance-based learning;

D O I：

10.1016/j.na.2010.06.035

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We propose a variant of two SVM regression algorithms expressly tailored in order to exploit additional information summarizing the relevance of each data item, as a measure of its relative importance w.r.t. the remaining examples. These variants, enclosing the original formulations when all data items have the same relevance, are preliminary tested on synthetic and real-world data sets. The obtained results outperform standard SVM approaches to regression if evaluated in light of the above mentioned additional information about data quality. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：2855 / 2867

页数：13

共 50 条

[11] Analysis of Support Vector Machines Regression
Hongzhi Tong
Di-Rong Chen
Lizhong Peng
[J]. Foundations of Computational Mathematics, 2009, 9 : 243 - 257
[12] Sparse multikernel support vector regression machines trained by active learning
Ceperic, V.
Gielen, G.
Baric, A.
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (12) : 11029 - 11035
[13] Relevance of support vector machines for stochastic mechanics
Hurtado, JE
[J]. COMPUTATIONAL FLUID AND SOLID MECHANICS 2003, VOLS 1 AND 2, PROCEEDINGS, 2003, : 2298 - 2301
[14] Support vector machines based active learning for the relevance feedback document retrieval
Onoda, Takashi
Murata, Hiroshi
Yamada, Seiji
[J]. 2006 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WORKSHOPS PROCEEDINGS, 2006, : 389 - +
[15] Regression Kernel for Prognostics with Support Vector Machines
Mathew, Josey
Luo, Ming
Pang, Chee Khiang
[J]. 2017 22ND IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2017,
[16] On the noise model of Support Vector Machines Regression
Pontil, M
Mukherjee, S
Girosi, F
[J]. ALGORITHMIC LEARNING THEORY, PROCEEDINGS, 2000, 1968 : 316 - 324
[17] Ensembles of support vector machines for regression problems
Lima, CAM
Coelho, ALV
Von Zuben, FJ
[J]. PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 2381 - 2386
[18] Smoothing support vector machines for ε-insensitive regression
Xiong, Jinzhi
Hu, Tianming
Hu, Jinlian
Li, Guangming
Peng, Hong
[J]. ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications, Vol 1, 2006, : 222 - 225
[19] Support vector machines regression with unbounded sampling
Tong, Hongzhi
Chen, Di-Rong
Yang, Fenghong
[J]. APPLICABLE ANALYSIS, 2019, 98 (09) : 1626 - 1635
[20] An Improved Active Learning Sparse Least Squares Support Vector Machines for Regression
Si Gangquan
Shi Jianquan
Guo Zhang
Gao Hong
[J]. 2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2015, : 4558 - 4562

← 1 2 3 4 5 →