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https://www.researchgate.net/publication/2801081_Adaptive_Margin_Support_Vector_Machines
Adaptive Margin Support Vector Machines. ... the theory of adaptive margin SVMs for classification problems is studied wherein by introducing a parameter the margin of separation gets adjusted. In ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.3250
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper we propose a new learning algorithm for classification learning based on the Support Vector Machine (SVM) approach. Existing approaches for constructing SVMs [12] are based on minimization of a regularized margin loss where the margin is treated equivalently for each …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.705.9875
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper we propose a new learning algorithm for classication learning based on the Support Vector Machine (SVM) approach. Existing ap-proaches for constructing SVMs [12] are based on minimization of a regularized margin loss where the margin is treated equivalently for each train-ing pattern.
http://papers.nips.cc/paper/2054-adaptive-nearest-neighbor-classification-using-support-vector-machines.pdf
Adaptive N earest Neighbor Classification using Support Vector Machines Carlotta Domeniconi, Dimitrios Gunopulos Dept. of Computer Science, University of California, Riverside, CA 92521 { carlotta, dg} @cs.ucr.edu Abstract The nearest neighbor technique is a simple and appealing method to address classification problems.
http://core.ac.uk/display/22721325
Adaptive Margin Support Vector Machines for Classification . By Ralf Herbrich and Jason Weston. Abstract. In this paper we propose a new learning algorithm for classification learning based on the Support Vector Machine (SVM) approach. Existing approaches for constructing SVMs [12] are based on minimization of a regularized margin loss where ...Author: Ralf Herbrich and Jason Weston
https://www.ijcaonline.org/research/volume131/number3/prakash-2015-ijca-907224.pdf
of adaptive margins for every training pattern. In Ref. [36], the first input space is mapped to a normalized feature space to expand the strength to noise. In Ref. [5], a robust support vector machine is proposed going for taking care of the over-fitting issue as Fuzzy support vector machine (FSVM).
https://www.depts.ttu.edu/transtech/documents/2.pdf
An Adaptive-Margin Support Vector Regression for Short-Term Traffic Flow Forecast . Dali Wei1, Hongchao Liu2,* 1 Civil and Environmental Department, Texas Tech University, 10th and Akron, Lubbock, TX 79409 . 2 Civil and Environmental Department, Texas Tech University, 10th and Akron, Lubbock, TX 79409 . Email: [email protected]
https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=6963&context=etd
Liu, Xin, "Data-Adaptive Kernel Support Vector Machine" (2017). Electronic Thesis and Dissertation Repository. 5036. https://ir.lib.uwo.ca/etd/5036 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been acceptedAuthor: Xin Liu
https://www.sciencedirect.com/topics/engineering/support-vector-machine
Support vector machines (SVM) [21–23] have been proposed as a powerful pattern classification technique which aims at maximizing the margin between two disjoint half spaces: the original input spaces for a linear classification problem or the higher dimensional feature space for a nonlinear classification problem.The maximal margin classifier represents the …
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/2801081_Adaptive_Margin_Support_Vector_Machines
Adaptive Margin Support Vector Machines. ... the theory of adaptive margin SVMs for classification problems is studied wherein by introducing a parameter the margin of separation gets adjusted. In ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.3250
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper we propose a new learning algorithm for classification learning based on the Support Vector Machine (SVM) approach. Existing approaches for constructing SVMs [12] are based on minimization of a regularized margin loss where the margin is treated equivalently for each training pattern.
http://core.ac.uk/display/22721325
Adaptive Margin Support Vector Machines for Classification . By Ralf Herbrich and Jason Weston. Abstract. In this paper we propose a new learning algorithm for classification learning based on the Support Vector Machine (SVM) approach. Existing approaches for constructing SVMs [12] are based on minimization of a regularized margin loss where ...Author: Ralf Herbrich and Jason Weston
https://www.ijcaonline.org/research/volume131/number3/prakash-2015-ijca-907224.pdf
Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines (2007) This paper [3], proposed another classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that tackle some issues that fall in support vector machines (SVMs) related to the face recognition. The proposed TAF-
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.256
BibTeX @MISC{Weston00adaptivemargin, author = {Jason Weston and Ralf Herbrich}, title = {Adaptive Margin Support Vector Machines}, year = {2000}}
https://www.depts.ttu.edu/transtech/documents/2.pdf
An Adaptive-Margin Support Vector Regression for Short-Term Traffic Flow Forecast . Dali Wei1, ... information to construct an adaptive ε-margin, in which both local and normalized factors ... 2.1 Support Vector Regression Support Vector Machine (SVM) is a …
https://link.springer.com/article/10.1007/s11280-019-00766-x
Dec 27, 2019 · Support Vector Machine (SVM) has been widely applied in real application due to its efficient performance in the classification task so that a large number of SVM methods have been proposed. In this paper, we present a novel SVM method by taking the dynamic graph learning and the self-paced learning into account. To do this, we propose utilizing self-paced learning to assign …Author: Rongyao Hu, Rongyao Hu, Xiaofeng Zhu, Xiaofeng Zhu, Yonghua Zhu, Jiangzhang Gan
https://www.sciencedirect.com/topics/engineering/support-vector-machine
Support vector machines (SVM) [21–23] have been proposed as a powerful pattern classification technique which aims at maximizing the margin between two disjoint half spaces: the original input spaces for a linear classification problem or the higher dimensional feature space for a nonlinear classification problem.The maximal margin classifier represents the classification problem as a …
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.
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