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https://www.researchgate.net/publication/235962490_An_Experimental_Evaluation_of_Pairwise_Adaptive_Support_Vector_Machines
The technique, called pairwise adaptive support vector machines (pa-SVM), is a one-vs-one multiclass classifier with each binary classifier optimized towards using the best (C,γ) parameter pair ...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5035294/
Although support vector machine (SVM) has become a powerful tool for pattern classification and regression, a major disadvantage is it fails to exploit the underlying correlation between any pair of data points as much as possible. Inspired by the modified pairwise constraints trick, in this paper ...Cited by: 2
https://core.ac.uk/display/33702326
Abstract. This paper describes and experimentally evaluates a new variation of multiclass classification using support vector machines. The technique, called pairwise adaptive support vector machines (pa-SVM), is a one-vs-one multiclass classifier with each binary classifier optimized towards using the best (C,γ) parameter pair to obtain the best correct classification rate.
https://dl.acm.org/citation.cfm?id=299094.299108
Javier Acevedo , Saturnino Maldonado , Philip Siegmann , Sergio Lafuente , Pedro Gil, Multi-class Support Vector Machines Based on Arranged Decision Graphs and Particle Swarm Optimization for Model Selection, Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II, April 11-14, 2007, Warsaw, PolandCited by: 1515
https://www.sciencedirect.com/science/article/pii/S0950705119303533
Nov 15, 2019 · Ensemble learning has been proven to give superior performance compared to single estimators. We propose a bagged ensemble comprising of support vector machines with a Gaussian kernel as a viable algorithm for the problem at hand. We report the results obtained on the three datasets mentioned above.Cited by: 1
https://courses.media.mit.edu/2006fall/mas622j/Projects/aisen-project/
Dec 15, 2006 · The support vector machine is a powerful tool for binary classification, capable of generating very fast classifier functions following a training period. There are several approaches to adopting SVMs to classification problems with three or more classes: Multiclass ranking SVMs, in which one SVM decision function attempts to classify all classes.
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000006252717
This paper describes and experimentally evaluates a new variation of multiclass classification using support vector machines. The technique, called pairwise adaptive support vector machines (pa-SVM), is a one-vs-one multiclass classifier with each binary classifier optimized towards using the best (C,γ) parameter pair to obtain the best correct classification rate.
http://dde.binghamton.edu/kodovsky/svm/lectures/svm_lecture_01.pdf
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https://www.researchgate.net/publication/233653188_Pattern_classification_of_fabric_defects_using_support_vector_machines
The technique, called pairwise adaptive support vector machines (pa-SVM), is a one-vs-one multiclass classifier with each binary classifier optimized towards using the best (C,γ) parameter pair ...
https://www.cs.cornell.edu/people/tj/svm_light/index.html
SVM light is an implementation of Support Vector Machines (SVMs) in C. The main features of the program are the following: ... Two examples are considered for a pairwise preference constraint only, if the value of "qid" is the same. For example, ... Adaptive precision tuning makes optimization more robust.
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