NDLI: Finding Potential Support Vectors in Separable Classification Problems

Content Provider	IEEE Xplore Digital Library
Author	Varagnolo, D. Del Favero, S. Dinuzzo, F. Schenato, L. Pillonetto, G.
Copyright Year	2012
Abstract	This paper considers the classification problem using support vector (SV) machines and investigates how to maximally reduce the size of the training set without losing information. Under separable data set assumptions, we derive the exact conditions stating which observations can be discarded without diminishing the overall information content. For this purpose, we introduce the concept of potential SVs, i.e., those data that can become SVs when future data become available. To complement this, we also characterize the set of discardable vectors (DVs), i.e., those data that, given the current data set, can never become SVs. Thus, these vectors are useless for future training purposes and can eventually be removed without loss of information. Then, we provide an efficient algorithm based on linear programming that returns the potential and DVs by constructing a simplex tableau. Finally, we compare it with alternative algorithms available in the literature on some synthetic data as well as on data sets from standard repositories.
Page Count	15
File Size	1521510
Starting Page	1799
Ending Page	1813
File Format	PDF
ISSN	2162237X
Volume Number	24
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	support vector machines Data discardability conditions discardable vectors linear programming potential support vectors separable data sets
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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	Dr. B. Sutradhar bsutra@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	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library 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

Complex Support Vector Machines for Regression and Quaternary Classification

Soft Margin Multiple Kernel Learning

Multiplicative Update Rules for Concurrent Nonnegative Matrix Factorization and Maximum Margin Classification

Pareto-Path Multitask Multiple Kernel Learning

Regularization Path for $nu$ -Support Vector Classification

Reducing the Number of Support Vectors of SVM Classifiers Using the Smoothed Separable Case Approximation

Efficient Algorithms for Exact Inference in Sequence Labeling SVMs

A Unifying Framework for Typical Multitask Multiple Kernel Learning Problems

Generalized SMO Algorithm for SVM-Based Multitask Learning

Finding Potential Support Vectors in Separable Classification Problems

Similar Documents

Complex Support Vector Machines for Regression and Quaternary Classification

Soft Margin Multiple Kernel Learning

Multiplicative Update Rules for Concurrent Nonnegative Matrix Factorization and Maximum Margin Classification

Pareto-Path Multitask Multiple Kernel Learning

Regularization Path for $nu$ -Support Vector Classification

Reducing the Number of Support Vectors of SVM Classifiers Using the Smoothed Separable Case Approximation

Efficient Algorithms for Exact Inference in Sequence Labeling SVMs

A Unifying Framework for Typical Multitask Multiple Kernel Learning Problems

Generalized SMO Algorithm for SVM-Based Multitask Learning

Finding Potential Support Vectors in Separable Classification Problems