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https://en.wikipedia.org/wiki/Support-vector_machine
Transductive support-vector machines were introduced by Vladimir N. Vapnik in 1998. Structured SVM [ edit ] SVMs have been generalized to structured SVMs , where the label space is structured and of possibly infinite size.
https://www.sciencedirect.com/topics/neuroscience/support-vector-machines
Support Vector Machines (SVMs) (Vapnik, 1998) are a family of learning algorithms, which is currently considered as one of the most efficient methods in many real world applications. The theory behind SVMs was developed in the 1960s and 1970s by Vapnik and Chervonenkis, but the first practical implementation of SVM was only published in the early 1990s.
http://image.diku.dk/imagecanon/material/cortes_vapnik95.pdf
Support-Vector Networks CORINNA CORTES [email protected] VLADIMIR VAPNIK [email protected] AT&T Bell Labs., Holmdel, NJ 07733, USA Editor: Lorenza Saitta Abstract. The support-vector network is a new learning machine for two-group classification problems. The
https://www.di.ens.fr/~mallat/papiers/svmtutorial.pdf
ideas behind Support Vector Machines (SVMs). The books (Vapnik, 1995; Vapnik, 1998) contain excellent descriptions of SVMs, but they leave room for an account whose purpose from the start is to teach. Although the subject can be said to have started in the late seventies (Vapnik, 1979), it is only now receiving increasing attention, and so the time
https://www.jstatsoft.org/article/view/v015i09/v15i09.pdf
Support Vector learning is based on simple ideas which originated in statistical learning theory (Vapnik 1998). The simplicity comes from the fact that Support Vector Machines (SVMs) apply a simple linear method to the data but in a high-dimensional feature space non-linearly
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf
1.1 Overview of Support Vector Machines Vladimir Vapnik invented Support Vector Machines in 1979 [19]. In its simplest, linear form, an SVM is a hyperplane that separates a set of positive examples from a set of negative examples with maximum margin (see figure 1). In the linear case, the margin is …Cited by: 3108
https://www.researchgate.net/publication/221621494_Support_Vector_Machines_Theory_and_Applications
Support Vector Machines (SVM) have been rece ntly developed in the framework of stati stical learning theory (Vapnik, 1998) (Cortes and Vapnik, 1995), and have been su ccessfully applied to a...
https://link.springer.com/article/10.1023%2FA%3A1022627411411
Sep 01, 1995 · The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space …Cited by: 38765
http://scholar.google.com/citations?user=vtegaJgAAAAJ&hl=en
Predicting time series with support vector machines KR Müller, AJ Smola, G Rätsch, B Schölkopf, J Kohlmorgen, V Vapnik International Conference on Artificial Neural Networks, 999-1004 , 1997
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