NDLI: Unsupervised and Semi-Supervised Bounded C-Support Vector Machines with Polyhedral Perturbations

Content Provider	IEEE Xplore Digital Library
Author	Kun Zhao Yongsheng Liu Naiyang Deng
Copyright Year	2009
Abstract	Support Vector Machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. These years two-class unsupervised and semi-supervised classification algorithms based on Bounded C-SVMs, Bounded n-SVMs and Lagrangian SVMs (LSVMs) respectively, which are relaxed to Semi-definite Programming (SDP), get good classification results. These support vector methods implicitly assume that training data in the optimization problems to be known exactly. But in practice, the training data are usually subjected to measurement noise. Zhao et al proposed robust version to Bounded C. SVMs, Bounded n-SVMs and Lagrangian SVMs (LSVMs) respectively with perturbations in convex polyhedrons and ellipsoids. The region of perturbation in the methods mentioned above is not general, and there are many perturbations in non-convex regions in practice. Therefore we proposed unsupervised and semi-supervised classification problems based on Bounded C. Support Vector Machines with general polyhedral perturbations. But the problem has difficulty to compute, we will find its semidefinite relaxation that can approximate it well. Numerical results confirm the robustness of the proposed method.
Starting Page	1015
Ending Page	1018
File Size	153454
Page Count	4
File Format	PDF
ISBN	9781424449095
e-ISBN	9781424457281
DOI	10.1109/ICISE.2009.1324
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Supervised learning Support vector machine classification Training data Optimization methods Machine learning Classification algorithms Noise robustness Noise measurement Lagrangian functions
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Unsupervised and Semi-Supervised Bounded V-Support Vector Machines with Polyhedral Perturbations

Unsupervised and Semi-supervised Lagrangian Support Vector Machines with Polyhedral Perturbations

Unsupervised Support Vector Machines with Perturbations

Robust Unsupervised and Semisupervised Bounded C-Support Vector Machines

Unsupervised and Semi-Supervised Two-class Support Vector Machines

A Novel Semi-Supervised SVM Based on Tri-Training

Semi-supervised Support Vector Data Description Multi-classification Learning Algorithm

A Survey of Semi-Supervised Learning Methods

Evolutionary Support Vector Regression Machines

Unsupervised and Semi-Supervised Bounded C-Support Vector Machines with Polyhedral Perturbations

Similar Documents

Unsupervised and Semi-Supervised Bounded V-Support Vector Machines with Polyhedral Perturbations

Unsupervised and Semi-supervised Lagrangian Support Vector Machines with Polyhedral Perturbations

Unsupervised Support Vector Machines with Perturbations

Robust Unsupervised and Semisupervised Bounded C-Support Vector Machines

Unsupervised and Semi-Supervised Two-class Support Vector Machines

A Novel Semi-Supervised SVM Based on Tri-Training

Semi-supervised Support Vector Data Description Multi-classification Learning Algorithm

A Survey of Semi-Supervised Learning Methods

Evolutionary Support Vector Regression Machines

Unsupervised and Semi-Supervised Bounded C-Support Vector Machines with Polyhedral Perturbations