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The copyright for any work remains with the original author unless otherwise specified.
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A parallel implementation of sequential minimal optimization on FPGA
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TL;DR: A parallel FPGA implementation of the training phase of a Support Vector Machine (SVM) is proposed using Sequential Minimal Optimization (SMO), which enables the resolution of a complex convex optimization problem using simple steps.
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About: This article is published in Microprocessors and Microsystems. The article was published on 01 Sep 2019. and is currently open access. The article focuses on the topics: Sequential minimal optimization & Support vector machine.
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Citations
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References
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
•Book
Neural Networks And Learning Machines
Simon Haykin
- 01 Jan 2010
TL;DR: Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together.
7.2K
Fast training of support vector machines using sequential minimal optimization, advances in kernel methods
J. C. Platt
- 01 Jan 1999
TL;DR: SMO breaks this large quadratic programming problem into a series of smallest possible QP problems, which avoids using a time-consuming numerical QP optimization as an inner loop and hence SMO is fastest for linear SVMs and sparse data sets.
5.7K
•Book
Fast training of support vector machines using sequential minimal optimization
John Platt
- 08 Feb 1999
TL;DR: In this article, the authors proposed a new algorithm for training Support Vector Machines (SVM) called SMO (Sequential Minimal Optimization), which breaks this large QP problem into a series of smallest possible QP problems.