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https://www.researchgate.net/publication/2891468_Incremental_Support_Vector_Machine_Construction
In this paper we present an incremental variant of the Twin Support Vector Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to …
https://cs.gmu.edu/~carlotta/publications/two.pdf
Incremental Support Vector Machine Construction Carlotta Domeniconi Dimitrios Gunopulos Computer Science Department University of California Riverside, CA 92521 carlotta,dg @cs.ucr.edu Abstract SVMs suffer from the problem of large memory require-ment and CPU time when trained in batch mode on large data sets. We overcome these limitations, and ...Cited by: 214
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.409.531
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): SVMs suffer from the problem of large memory requirement and CPU time when trained in batch mode on large data sets. We overcome these limitations, and at the same time make SVMs suitable for learning with data streams, by constructing incremental learning algorithms.
https://core.ac.uk/display/21684405
Incremental Support Vector Machine Construction . By Carlotta Domeniconi and Dimitrios Gunopulos. ... We first introduce and compare different incremental learning techniques, and show that they are capable of producing performance results similar to the batch algorithm, and in some cases superior condensation properties. ...Author: Carlotta Domeniconi and Dimitrios Gunopulos
https://core.ac.uk/display/23799740
Incremental support vector machine construction . By Carlotta Domeniconi and Dimitrios Gunopulos. Download PDF (227 KB) Abstract. SVMs suffer from the problem of large memory requirement and CPU time when trained in batch mode on large data sets. ... We first introduce and compare different incremental learning techniques, and show that they ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1440
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): SVMs suffer from the problem of large memory requirement and CPU time when trained in batch mode on large data sets. The training process, in fact, involves the solution of a quadratic programming problem. We overcome these limitations, and at the same time make SVMs suitable for learning with data streams, by ...
https://isn.ucsd.edu/pub/papers/nips00_inc.pdf
An on-linerecursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the Kuhn-Tucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is re-
http://citeseer.ist.psu.edu/showciting?cid=281504
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537225/
Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed.Cited by: 6
https://link.springer.com/chapter/10.1007/978-3-319-89803-2_12
Jul 29, 2018 · The aim of this paper is to present a review of methods for incremental Support Vector Machines (SVM) learning and their adaptation for data stream classification in evolving environments. We formalize a taxonomy of these methods based on their characteristics and …Author: Isah Abdullahi Lawal
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