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http://image.diku.dk/imagecanon/material/cortes_vapnik95.pdf
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 machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space.
https://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=2235455
ABSTRACT: Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge.
https://link.springer.com/article/10.1007%2FBF00994018
Sep 01, 1995 · Thesupport-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 a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine…Cited by: 38765
http://scholar.google.com/citations?user=vtegaJgAAAAJ&hl=en
1995: Support-vector networks. C Cortes, V Vapnik. Machine learning 20 (3), 273-297 ... CJC Burges, L Kaufman, AJ Smola, V Vapnik. Advances in neural information processing systems, 155-161, 1997. 3176: 1997: Support vector method for function approximation, regression estimation and signal processing ... Support vector machines for histogram ...
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://www.sciencedirect.com/topics/neuroscience/support-vector-machines
Trait values for unknown individuals are then assessed relative to this function and classified accordingly (Cortes and Vapnik, 1995). Support vector machines are designed for pairwise comparisons, but recent work has demonstrated that they can effectively estimate ancestry between more than two groups (Hefner et al., 2015).
http://image.diku.dk/imagecanon/material/cortes_vapnik95.pdf
SUPPORT-VECTOR NETWORKS 275 Figure 2. An example of a separabl e problem in a 2 dimensional space. The support vectors , marked with grey squares, define the margin of largest separation between the two classes.
http://scholar.google.com/citations?user=vtegaJgAAAAJ&hl=en
This "Cited by" count includes citations to the following articles in Scholar. ... 1995: Support-vector networks. C Cortes, V Vapnik. Machine learning 20 (3), 273-297, 1995. 39033: 1995: ... Support vector machines for histogram-based image classification. O Chapelle, P Haffner, VN Vapnik ...
https://link.springer.com/article/10.1007%2FBF00994018
Sep 01, 1995 · The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated.Cited by: 38765
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 a …Cited by: 38765
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
https://www.researchgate.net/publication/221621494_Support_Vector_Machines_Theory_and_Applications
This chapter presents a summary of the issues discussed during the one day workshop on ”Support Vector Machines (SVM) Theory and Applications” organized as …
https://www.sciencedirect.com/topics/neuroscience/support-vector-machines
Trait values for unknown individuals are then assessed relative to this function and classified accordingly (Cortes and Vapnik, 1995). Support vector machines are designed for pairwise comparisons, but recent work has demonstrated that they can effectively estimate ancestry between more than two groups (Hefner et al., 2015).
https://isn.ucsd.edu/courses//776/slides/kernel-learning.pdf
Statistical Learning Theory and Support Vector Machines OUTLINE ... Cortes and Vapnik, 1995. G. Cauwenberghs 520.776 Learning on Silicon Kernel Logistic Probability Regression subject to: 0 , with ( ) ln (1 )ln(1 ) min: 12 ( ) 2, y H a a a a a Q C H i i i i C i ij j j i kLR b
http://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://en.wikipedia.org/wiki/Vladimir_Vapnik
At the end of 1990, Vladimir Vapnik moved to the USA and joined the Adaptive Systems Research Department at AT&T Bell Labs in Holmdel, New Jersey. While at AT&T, Vapnik and his colleagues did work on the support-vector machine.Alma mater: Institute of Control Sciences, …
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