Searching for C Cortes And V Vapnik Support Vector Networks Machine Learning information? Find all needed info by using official links provided below.
https://link.springer.com/article/10.1007%2FBF00994018
Sep 01, 1995 · Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. ... Support-vector networks. Corinna Cortes 1 & ... C., Vapnik, V. Support-vector networks ...Cited by: 38765
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/10.1023%2FA%3A1022627411411
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 linear decision surface is constructed.Cited by: 38765
http://homepages.rpi.edu/~bennek/class/mmld/papers/svn.pdf
output from the 4 hidden units weights of the 4 hidden units dot−products weights of the 5 hidden units dot−products dot−product perceptron output
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: ... This research aims to assess and compare performance of single and ensemble classifiers of Support Vector Machine (SVM) and Classification Tree (CT) by using simulation data. ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.9362
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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 linear decision surface is constructed.
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