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https://www.sciencedirect.com/science/article/pii/S0377221707011150
We propose a proximal version of the knowledge based support vector machine formulation, termed as knowledge based proximal support vector machines …Cited by: 26
https://www.sciencedirect.com/science/article/abs/pii/S0377221707011150
Jun 16, 2009 · We propose a proximal version of the knowledge based support vector machine formulation, termed as knowledge based proximal support vector machines (KBPSVMs) in the sequel, for binary data classification. The KBPSVM classifier incorporates prior knowledge in the form of multiple polyhedral sets, and determines two parallel planes that are kept ...Cited by: 26
https://www.researchgate.net/publication/23648421_Knowledge_based_proximal_support_vector_machines
We propose a proximal version of the knowledge based support vector machine formulation, termed as knowledge based proximal support vector machines (KBPSVMs) in the sequel, for binary data ...
https://research.cs.wisc.edu/dmi/svm/psvm/
Iinstead of a standard support vector machine that classifies points by assigning them to one of two disjoint half-spaces, PSVM classifies points by assigning them to the closest of two parallel planes. For more information, see our paper Proximal Support Vector Machines. SVMs are an optimization based approach for solving machine learning ...
https://www.deepdyve.com/lp/elsevier/knowledge-based-proximal-support-vector-machines-ZDPB3XwLh0
Jun 16, 2009 · We propose a proximal version of the knowledge based support vector machine formulation, termed as knowledge based proximal support vector machines (KBPSVMs) in the sequel, for binary data classification. The KBPSVM classifier incorporates prior knowledge in the form of multiple polyhedral sets, and determines two parallel planes that are kept as distant from each other …
http://pages.cs.wisc.edu/~gfung/proximal_knowledge_based_classification.pdf
another recent work, prior knowledge is incorporated by adding additional points labeled based on the prior knowledge to the dataset [19]. In Section 3 we describe our nonlinear kernel classification formulation that incorporates nonlinear prior knowledge as linear equalities in a proximal support vector machine formulation which leads
http://ftp.cs.wisc.edu/pub/dmi/tech-reports/06-05.pdf
tion, proximal support vector machines 1 Introduction Prior knowledge has been used effectively in improv-ing classification for both linear [2] and nonlinear [3] kernel classifiers, as well as for nonlinear kernel approximation [4, 5]. In these applications, prior knowledge was …
http://ftp.cs.wisc.edu/machine-learning/shavlik-group/kunapuli.ecml10.pdf
Online Knowledge-Based Support Vector Machines Gautam Kunapuli∗, Kristin P. Bennett†, Amina Shabbeer†, Richard Maclin‡ and Jude Shavlik∗ ∗University of Wisconsin-Madison, †Rensselaer Polytechnic Insitute, and ‡University of Minnesota, Duluth Abstract. Prior knowledge, in the form of simple advice rules, can
https://dl.acm.org/doi/10.1145/502512.502527
Proximal support vector machine classifiers. ... Previous Chapter Next Chapter. ABSTRACT. Instead of a standard support vector machine (SVM) that classifies points by assigning them to one of two disjoint half-spaces, points are classified by assigning them to the closest of two parallel planes (in input or feature space) that are pushed apart ...
http://www.pnas.org/content/97/1/262
Jan 04, 2000 · The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data.
https://www.deepdyve.com/lp/wiley/proximal-knowledge-based-classification-FdP3T4U5NE
Mar 01, 2009 · These equalities are then included into a proximal nonlinear kernel classification formulation (G. Fung and O. L. Mangasarian, Proximal support vector machine classifiers, in Proceedings KDD‐2001: Knowledge Discovery and Data Mining, F. Provost and R. Srikant (eds), San Francisco, CA, New York, Association for Computing Machinery) that is ...
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000001527618
Basic support vector machine learning is applied to local network fault knowledge acquisition, and incremental PSVM is improved to be adapted to global dynamic network fault knowledge acquisition. Simulations indicate the correctness and efficiency of our method and the global network fault knowledge acquisition based on proximal support vector machine is still to be improved further.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.2088
BibTeX @TECHREPORT{Tveit03parallelizationof, author = {Amund Tveit}, title = {Parallelization of the Incremental Proximal Support Vector Machine Classifier using a Heap-based Tree Topology}, institution = {In Parallel and Distributed computing for Machine Learning. In conjunction with the 14th European Conference on Machine Learning (ECML’03) and 7th European Conference on Principles …
https://link.springer.com/chapter/10.1007/978-3-540-45228-7_42
Abstract. This paper presents an efficient approach for supporting decremental learning for incremental proximal support vector machines (SVM). The presented decremental algorithm based on decay coefficients is compared with an existing window-based decremental algorithm, and is shown to perform at a similar level in accuracy, but providing significantly better computational performance.
https://datascience.stackexchange.com/questions/62649/what-is-the-difference-between-ls-svm-and-p-svm/62869
Proximal support vector machines and related approaches (Fung & Mangasarian, 2001; Suykens & Vandewalle, 1999) can be interpreted as ridge regression applied to classification problems (Evgeniou, Pontil, & Poggio, 2000). Extensive computational results have shown the effectiveness of PSVM for two-class classification problems where the separating plane is constructed in time that can be as little as …
https://www.deepdyve.com/lp/elsevier/a-proximal-classifier-with-consistency-DFbsLT220X
Sep 01, 2013 · Read "A proximal classifier with consistency, Knowledge-Based Systems" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
https://dl.acm.org/citation.cfm?id=593463
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global.
http://pages.cs.wisc.edu/~gfung/
Incremental Support Vector Machines Classification , Second SIAM International Conference on Data Mining, April 11-13, 2002, Arlington, VA. Glenn Fung and O.L. Mangasarian Proximal Support Vector Machines Classifiers , SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 26-29, 2001, San Francisco, CA.
https://www.worldscientific.com/doi/abs/10.1142/S0218213007003163
Support vector machine-based decision tree for snow cover extraction in mountain areas using high spatial resolution remote sensing image Liujun Zhu, Pengfeng Xiao, Xuezhi Feng, Xueliang Zhang and …
https://www.semanticscholar.org/paper/Financial-Time-Series-Forecasting-Using-Support-Gui-Wei/182c4e2dc048454bada5b1bf7431bf3536e0b69d
In this paper, we transform the financial time series into fuzzy grain particle sequences, and use support vector machine regression to regress the upper and lower bounds of the fuzzy particles, and then apply regression model single-step prediction on the upper and lower bounds, which will …
https://arxiv.org/pdf/1501.00728
Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine M. H. Marghny Computer Science Department, Faculty of Computers and Information, Assiut University, Egypt. Rasha M. Abd El-Aziz Computer Science Department, Faculty of Science, Assiut University, Egypt. Ahmed I. Taloba
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