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https://www.sciencedirect.com/science/article/pii/S095741740800674X
Support vector machine (SVM) is a novel machine learning methodology based on statistical learning theory (SLT), which has considerable features including the fact that requirement on kernel and nature of the optimization problem results in a uniquely global optimum, high generalization performance, and prevention from converging to a local ...Cited by: 190
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.6950
Generalization Performance of Support Vector Machines and Other Pattern Classifiers (1998) ... {Peter Bartlett and John Shawe-taylor}, title = {Generalization Performance of Support Vector Machines and Other ... support vector machine generalization performance pattern classifier support vector ...
https://www.sciencedirect.com/topics/computer-science/generalization-performance
This also has an influence on the generalization properties, and indeed, SVMs tend to exhibit good generalization performance. We will return to this issue at the end of Chapter 5. A major limitation of the support vector machines is that up to now there has been no efficient practical method for selecting the best kernel function.
http://www.bme.teiath.gr/medisp/pdfs/GLOTSOS_2004_ICSCCE_Evaluating_the_Generalization_Performance.pdf
EVALUATING THE GENERALIZATION PERFORMANCE OF A SUPPORT VECTOR MACHINE BASED CLASSIFICATION METHODOLOGY IN BRAIN TUMOR ASTROCYTOMAS GRADING Dimitris Glotsos †, Panagiota Spyridonos , Giota Ravazoula††, Giannis Kalatzis†††, StavrosTsantis†, George Nikiforidis†, Dionisis Cavouras†††
http://tka4.org/materials/lib/Articles-Books/Speech%20Recognition/from%20Nickolas/SVM%20and%20Generalization.pdf
Support Vector Machine and Generalization H1 H2 Fig. 2. Separated super plane and margin of the linear threshold element (iand indicate class 1 and class1 samples. At the stationary point, the followingrelationshipis estab-and indicate support vectors.)vector using the linear discriminant function as …
https://dl.acm.org/citation.cfm?id=299098
Theodoros Evgeniou , Massimiliano Pontil, Support Vector Machines: Theory and Applications, Machine Learning and Its Applications, Advanced Lectures, p.249-257, January 01, 2001 Ingo Steinwart, On the influence of the kernel on the consistency of support vector machines, The Journal of Machine Learning Research, 2, p.67-93, 3/1/2002Cited by: 371
http://alex.smola.org/papers/2001/WilSmoSch01.pdf
induced by the kernel function used by the machine. As a conse-quence, we are able to theoretically explain the effect of the choice of kernel function on the generalization performance of support vector machines. Index Terms— Covering numbers, -entropy, kernel methods, linear operators, metric entropy, statistical learning theory,
https://towardsdatascience.com/the-complete-guide-to-support-vector-machine-svm-f1a820d8af0b
Jul 29, 2019 · The support vector machine is a generalization of a classifier called maximal margin classifier. The maximal margin classifier is simple, but it cannot be applied to the majority of datasets, since the classes must be separated by a linear boundary.Author: Marco Peixeiro
http://people.cs.uchicago.edu/~vikass/s3vm.pdf
large collection of unlabeled data jointly with a few labeled examples for improving generalization performance. The design of Support Vector Machines (SVMs) that can handle partially labeled data sets has naturally been a vigorously active subject. A major body of work is based on the following idea:
https://link.springer.com/chapter/10.1007/3-540-44673-7_12
Sep 20, 2001 · Bartlett P. and Shawe-Taylor J., “Generalization performance of support vector machine and other pattern classifiers”, In C. ~Burges B. ~Scholkopf, editor, “Advances in Kernel Methods-Support Vector Learning”, pp. 43–55, MIT press, 1998.Cited by: 24
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