Simplified Support Vector Decision Rules

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CiteSeerX — Simplified Support Vector Decision Rules

    http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.9934
    CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): A Support Vector Machine (SVM) is a universal learning machine whose decision surface is parameterized by a set of support vectors, and by a set of corresponding weights. An SVM is also characterized by a kernel function. Choice of the kernel determines whether the resulting SVM is a polynomial classifier, a two …

Simplified Support Vector Decision Rules - CORE

    http://core.ac.uk/display/24354528
    Simplified Support Vector Decision Rules . By Chris J.C. Burges. Abstract. A Support Vector Machine (SVM) is a universal learning machine whose decision surface is parameterized by a set of support vectors, and by a set of corresponding weights. An SVM is also characterized by a kernel function. Choice of the kernel determines whether the ...Author: Chris J.C. Burges

A tutorial on support vector regression SpringerLink

    https://link.springer.com/article/10.1023%2FB%3ASTCO.0000035301.49549.88
    Simplified support vector decision rules. In L. Saitta (Ed.), Proceedings of the International Conference on Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, pp. 71-77. ... Smola A.J. and Schölkopf B. 1998b. A tutorial on support vector regression. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of ...Cited by: 9551

A Tutorial on Support Vector Machines for Pattern ...

    https://link.springer.com/article/10.1023%2FA%3A1009715923555
    Burges, C.J.C. Simplified support vector decision rules. In Lorenza Saitta, editor, Proceedings of the Thirteenth International Conference on Machine Learning, pages 71–77, Bari, Italy, 1996. Morgan Kaufman. Google ScholarCited by: 21704

A Tutorial on Support Vector Machines for Pattern Recognition

    https://rd.springer.com/article/10.1023%2FA%3A1009715923555
    Jun 01, 1998 · 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.Cited by: 21704

Classification Algorithm Support Vector Machine

    https://www.analyticsvidhya.com/blog/2014/10/support-vector-machine-simplified/
    Oct 03, 2014 · This article explains about powerful classification algorithm Support Vector Machine (SVM), its working and its uses. Support Vector machine can be very effective. ... Support Vector Machine – Simplified. Tavish Srivastava ... Using a sample of this population, you want to create some set of rules which will guide us the gender class for rest ...

An Efficient Method for Simplifying Decision Functions of ...

    https://www.researchgate.net/publication/220234172_An_Efficient_Method_for_Simplifying_Decision_Functions_of_Support_Vector_Machines
    An Efficient Method for Simplifying Decision Functions of Support Vector Machines Article in IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences 89-A(10):2795 ...

Simplifying support vector solutions

    http://jmlr.csail.mit.edu/papers/volume2/downs01a/downs01a.pdf
    EXACT SIMPLIFLIFICATION OF SUPPORT VECTOR SOLUTIONS 295 modified according to (5) in order to obtain the simplified representation. But this is a very simple modification that can be applied to any linearly dependent support vector that is identified.

Support-vector machine - Wikipedia

    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 …

Kernel PCA for novelty detection - ScienceDirect

    https://www.sciencedirect.com/science/article/pii/S0031320306003414
    Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the …Cited by: 511



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