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https://www.sciencedirect.com/science/article/pii/S0020025513006592
A preliminary version of the paper, “Exact Indexing for Support Vector Machines”, appeared in Proc. ACM SIGMOD 2011. However, this submission has substantially extended the previous paper and contains new and major-value added technical contribution in comparison with …Cited by: 3
https://dl.acm.org/citation.cfm?id=1989398
SVM (Support Vector Machine) is a well-established machine learning methodology popularly used for classification, regression, and ranking. Recently SVM has been actively researched for rank learning and applied to various applications including search engines or relevance feedback systems.Cited by: 10
https://www.researchgate.net/publication/221214704_IKernel_Exact_indexing_for_support_vector_machines
Support Vector Machines (SVMs) have been adopted by many data-mining and information-retrieval applications for learning a mining or query concept, and then retrieving the "top-k" best matches to ...
https://www.newtechdojo.com/learn-support-vector-machine-using-excel/
Dec 28, 2017 · Beginner guide to learn the most well known and well-understood algorithm in statistics and machine learning. In this post, you will discover the Support Vector Machine Algorithm, how it works using Excel, application and pros and cons.
https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html
Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. In this section, we will develop the intuition behind support vector machines and their use in classification problems. We begin with the standard imports:
https://isn.ucsd.edu/pub/papers/nips00_inc.pdf
Incremental and Decremental Support Vector Machine Learning Gert Cauwenberghs CLSP, ECE Dept. Johns Hopkins University Baltimore, MD 21218 [email protected] Tomaso Poggio CBCL, BCS Dept. Massachusetts Institute of Technology Cambridge, MA 02142 [email protected] Abstract An on-linerecursive algorithm for training support vector machines, one
https://scikit-learn.org/stable/modules/svm.html
Support Vector Machine algorithms are not scale invariant, so it is highly recommended to scale your data. For example, scale each attribute on the input vector X to [0,1] or [-1,+1], or standardize it to have mean 0 and variance 1. Note that the same scaling must be applied to the test vector to obtain meaningful results.
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/3297557_KDX_An_Indexer_for_Support_Vector_Machines
Support Vector Machines (SVMs) have been adopted by many data-mining and information-retrieval applications for learning a mining or query concept, and then retrieving the "top-k" best matches to ...
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