Results 21 to 30 of about 469,122 (324)

Extensions of the SVM Method to the Non-Linearly Separable Data [PDF]

Informatică economică, 2013
The main aim of the paper is to briefly investigate the most significant topics of the currently used methodologies of solving and implementing SVM-based classifier.
Luminita STATE, Catalina COCIANU, Cristian USCATU, Marinela MIRCEA   +3 more
doaj   +1 more source

Comment on "Support Vector Machines with Applications"

, 2006
Comment on "Support Vector Machines with Applications" [math.ST/0612817]Comment: Published at http://dx.doi.org/10.1214/088342306000000475 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.
Bartlett, Peter L., Jordan, Michael I., McAuliffe, Jon D.   +2 more
core   +4 more sources

Robust relative margin support vector machines

Journal of Algorithms & Computational Technology, 2017
Recently, a class of classifiers, called relative margin machine, has been developed. Relative margin machine has shown significant improvements over the large margin counterparts on real-world problems.
Yunyan Song, Wenxin Zhu, Yingyuan Xiao, Ping Zhong   +3 more
doaj   +1 more source

Oblique Support Vector Machines

Informatica, 2005
In this paper we propose a modified framework of support vector machines, called Oblique Support Vector Machines(OSVMs), to improve the capability of classification. The principle of OSVMs is joining an orthogonal vector into weight vector in order to rotate the support hyperplanes.
null Chih-Chia Yao, null Pao-Ta Yu
openaire   +2 more sources

Breakdown Point of Robust Support Vector Machines

Entropy, 2017
Support vector machine (SVM) is one of the most successful learning methods for solving classification problems. Despite its popularity, SVM has the serious drawback that it is sensitive to outliers in training samples.
Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda   +2 more
doaj   +1 more source

The complexity of quantum support vector machines [PDF]

Quantum
Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models
Gian Gentinetta, Arne Thomsen, David Sutter, Stefan Woerner   +3 more
doaj   +1 more source

Cascade Support Vector Machines with Dimensionality Reduction

Applied Computational Intelligence and Soft Computing, 2015
Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing.
Oliver Kramer
doaj   +1 more source

Selection of Support Vector Candidates Using Relative Support Distance for Sustainability in Large-Scale Support Vector Machines

Applied Sciences, 2020
Support vector machines (SVMs) are a well-known classifier due to their superior classification performance. They are defined by a hyperplane, which separates two classes with the largest margin.
Minho Ryu, Kichun Lee
doaj   +1 more source

Beamforming Using Support Vector Machines [PDF]

, 2005
Support vector machines (SVMs) have improved generalization performance over other classical optimization techniques. Here, we introduce an SVM-based approach for linear array processing and beamforming.
Christodoulou, Christos G., Martínez-Ramón, Manel, Xu, Nan   +2 more
core   +2 more sources

Coupled Least Squares Support Vector Ensemble Machines

Information, 2019
The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications.
Dickson Keddy Wornyo, Xiang-Jun Shen
doaj   +1 more source