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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.
http://www.smartlab.ws/files/pubblications/IJP/R021.pdf
A Digital Architecture for Support Vector Machines: Theory, Algorithm, and FPGA Implementation Davide Anguita, Member, IEEE, Andrea Boni, and Sandro Ridella, Member, IEEE Abstract— In this paper, we propose a digital architecture for support vector machine (SVM) learning and discuss its implemen-tation on a field programmable gate array (FPGA).
https://dl.acm.org/doi/10.1109/TNN.2003.816033
In this paper, we propose a digital architecture for support vector machine (SVM) learning and discuss its implementation on a field programmable gate array (FPGA). We analyze briefly the quantizat...
https://www.engineeringbigdata.com/support-vector-machine-algorithm/
There are a number of machine learning algorithms in the world that have a vast number of uses. Few of these algorithms have the same utility, however, as the support vector machine. A support vector machine may not sound as simple or as straightforward as a decision tree or a linear regression algorithm. But that does not mean it is not as ...
https://towardsdatascience.com/a-practical-guide-to-interpreting-and-visualising-support-vector-machines-97d2a5b0564e
Jan 12, 2019 · The Support Vector Machine (SVM) is the only linear model which can classify data which is not linearly separable. You might be asking how the SVM which is a linear model can fit a linear classifier to non linear data. Intuitively with a simple linear regression model we may manually engineer x, x², x³,… features to attempt to achieve a fit ...
http://www.cs.northwestern.edu/~pardo/courses/eecs349/readings/support_vector_machines4.pdf
A Tutorial on Support Vector Machines for Pattern Recognition CHRISTOPHER J.C. BURGES [email protected] Bell Laboratories, Lucent Technologies Editor: Usama Fayyad Abstract. The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization.
https://arxiv.org/pdf/1712.03541.pdf
An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification Abien Fred M. Agarap [email protected] ABSTRACT Convolutional neural networks (CNNs) are similar to “ordinary” neural networks in …
https://kolmogorov.fsc.stevens.edu/hff_conference2013/images/Kercheval_Alec_HF2013.pdf
Support Vector Machines Limit Order Book Dynamics and Model Architecture Experimental Results Modeling high-frequency limit order book dynamics with support vector machines Alec Kercheval and Yuan Zhang Department of Mathematics Florida State University October 24, 2013 1/29.
https://homepage.cs.uri.edu/faculty/hamel/pubs/hamel-CIBCB-2006-v2.pdf
Visualization of support vector models is a difficult problem due to the high-dimensionality of the typical dataset. 1 Here we only consider support vector machine classification. Here we propose a visualization technique of support vector machines that makes use of unsupervised learning in order to
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