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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://link.springer.com/article/10.1007%2FBF00994018
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
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.
http://www.sciepub.com/reference/47107
Cortes, C. and Vapnik, V., “Support-Vector Networks,” Machine Learning 20(3). 273-297. 1995. has been cited by the following article: Article. Comparison of Single and Ensemble Classifiers of Support Vector Machine and Classification Tree. Iut Tri Utami 1,, Bagus Sartono 2, Kusman Sadik 2.
https://en.wikipedia.org/wiki/Corinna_Cortes
Cortes' research covers a wide range of topics in machine learning, including support vector machines and data mining. In 2008, she jointly with Vladimir Vapnik received the Paris Kanellakis Theory and Practice Award for the development of a highly effective algorithm for supervised learning known as support vector machines (SVM).Alma mater: Niels Bohr Institute, University of Rochester
https://www.sciencedirect.com/topics/neuroscience/support-vector-machines
Support vector machines are perhaps the most similar of the machine learning methods to the discriminant analyses traditionally employed with metric analysis. Individuals in a training set are arranged in n -dimensional space, and a function, linear or otherwise, that best separates the data by levels of the categorical variable is calculated ( Cortes and Vapnik, 1995; Hefner and Ousley, 2014 ).
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