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Statistical thresholding method for infrared images

Iwasawa decomposition: a new approach to 2D affine registration problem

Self-organizing maps and boundary effects: quantifying the benefits of torus wrapping for mapping SOM trajectories

Line segment detection using weighted mean shift procedures on a 2D slice sampling strategy

Classification through incremental max–min separability

Identifying distributed and overlapping clusters of hemodynamic synchrony in fMRI data sets

MLiT: mixtures of Gaussians under linear transformations

A three-step unsupervised neural model for visualizing high complex dimensional spectroscopic data sets

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Classification through incremental max–min separability

Content Provider	Springer Nature Link
Author	Bagirov, Adil M. Ugon, Julien Webb, Dean Karasözen, B.
Copyright Year	2010
Abstract	Piecewise linear functions can be used to approximate non-linear decision boundaries between pattern classes. Piecewise linear boundaries are known to provide efficient real-time classifiers. However, they require a long training time. Finding piecewise linear boundaries between sets is a difficult optimization problem. Most approaches use heuristics to avoid solving this problem, which may lead to suboptimal piecewise linear boundaries. In this paper, we propose an algorithm for globally training hyperplanes using an incremental approach. Such an approach allows one to find a near global minimizer of the classification error function and to compute as few hyperplanes as needed for separating sets. We apply this algorithm for solving supervised data classification problems and report the results of numerical experiments on real-world data sets. These results demonstrate that the new algorithm requires a reasonable training time and its test set accuracy is consistently good on most data sets compared with mainstream classifiers.
Starting Page	165
Ending Page	174
Page Count	10
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	14
Issue Number	2
e-ISSN	1433755X
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-12-03
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classification Data mining Data analysis Supervised learning Piecewise linear classifier Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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
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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
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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Max-min separability : incremental approach and application to supervised data classification

Classification through incremental max–min separability

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