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https://di.ku.dk/forskning/Publikationer/tekniske_rapporter/2005/05-01.pdf
to develop a distributed machine learning framework capable of solving many learning tasks yet configurable toward additional distribution-related constraints, a certain algorithmic family seems particularly interesting: support vector machines (SVMs). Its main asset in this context—as a dual
https://www.cs.uic.edu/~cornelia/posters/aaai05.pdf
for learning exact SVMs from distributed data sources. Support Vector Machine Algorithm Support Vector Machines (SVM) algorithm (Burges 1998) has been shown to be one of the most effective machine learning algorithms. It gives very good results in terms of accuracy when the data are linearly or non-linearly separa-ble.
https://machinelearningmastery.com/support-vector-machines-for-machine-learning/
Support Vector Machines and how the learning algorithm can be reformulated as a dot-product kernel and how other kernels like Polynomial and Radial can be used. How you can use numerical optimization to learn the hyperplane and that efficient implementations use an alternate optimization scheme called Sequential Minimal Optimization.
https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47
Jun 07, 2018 · Support vector machine is another simple algorithm that every machine learning expert should have in his/her arsenal. Support vector machine is highly preferred by many as it produces significant accuracy with less computation power. Support Vector Machine, abbreviated as SVM can be used for both regression and classification tasks.Author: Rohith Gandhi
https://www.r-bloggers.com/machine-learning-using-support-vector-machines/
Apr 19, 2017 · Support Vector Machines (SVM) is a data classification method that separates data using hyperplanes. The concept of SVM is very intuitive and easily understandable. If we have labeled data, SVM can be used to generate multiple separating hyperplanes such that the data space is divided into segments and each segment contains only one kind of data.
http://www.m-hikari.com/ijco/ijco2017/ijco1-2017/p/winters-hiltIJCO1-2017-2.pdf
Distributed SVM learning and support vector reduction 95 Once α 2 new is obtained, the constraint α 2 ≤ C must be re-verified in conjunction with the αβ yβ = 0 constraint. If the L (α 2;αβ' ≥ 3) maximization leads to a α 2 new that grows too large, the new α 2 must …
https://www.researchgate.net/publication/3303738_Distributed_Support_Vector_Machines
With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and...
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.
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