Results 161 to 170 of about 142,623 (210)

RD-SVM: A resilient distributed support vector machine

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
Support vector machines (SVMs) are one of the most widely used supervised learning algorithms for classification problems. Recent years have witnessed an increasing interest in distributed variants of SVMs, in which the (labeled) training data is distributed across different nodes.
Zhixiong Yang 0002, Waheed U. Bajwa
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Support Vector Machines (SVMs)

2015
This Chapter details a class of learning mechanisms known as the Support Vector Machines (SVMs). We start by giving the machine learning framework, define and introduce the concepts of linear classifiers, and describe formally the SVMs as large margin classifiers. We focus on the convex optimization problem and in particular we deal with the Sequential
Noel Lopes, Bernardete Ribeiro
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Sequential bootstrapped support vector machines a SVM accelerator

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2006
Support vector machine has obtained much success in machine learning. But it requires to solve a quadratic optimization (QP) problem so that its training time increases dramatically with the increase of training set. Hence, standard SVM with batch learning has difficulty in handling large scale problems.
Xuchun Li, Yan Zhu, Eric Sung
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Gases identification with Support Vector Machines technique (SVMs)

2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2014
Air pollution is an olfactory pollution because many polluting gases have a strong odor even at low concentrations. These pollutants are natural or anthropogenic emission sources. This pollution has many harmful effects on human health or upon the environment. So it is necessary to detect the pollution to reduce its effects.
Souhir Bedoui, Hekmet Samet, Mounir Samet, Abdennaceur Kachouri   +3 more
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Support vector machines (SVMs) for monitoring network design

Groundwater, 2005
Abstract In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized error (risk) model, rather than just the mean square error over a training set.
Asefa, T. M., Kemblowski, M., Urroz, G., McKee, Mac   +3 more
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Fusing binary support vector machines (SVM) into multiclass SVM

SPIE Proceedings, 2006
Multi-class support vector machine by fusing a class of binary support vector machines is proposed. The classifier fusion approaches include simple combination method such as Maximum, Minimum, Product, Mean, Median and Major Voting. Dempster-Shafer fusion method is also presented as well as KNN and Neural network approaches.
Zilu Ying, Jingwen Li, Youwei Zhang
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Parallel randomized sampling for support vector machine (SVM) and support vector regression (SVR)

Knowledge and Information Systems, 2007
A parallel randomized support vector machine (PRSVM) and a parallel randomized support vector regression (PRSVR) algorithm based on a randomized sampling technique are proposed in this paper. The proposed PRSVM and PRSVR have four major advantages over previous methods.
Yumao Lu, Vwani P. Roychowdhury
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Support Vector Machines (SVM)

2018
In statistical learning theory (regression, classification, etc.) there are many regression models, such as algebraic polynomials,
Joseph L. Awange, Béla Paláncz, Robert H. Lewis, Lajos Völgyesi   +3 more
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EuDiC SVM: A novel support vector machine classification algorithm

Intelligent Data Analysis, 2016
The Support Vector Machine (SVM) is a powerful technique for data classification. For linearly separable data points, the SVM constructs an optimal separating hyper-plane as a decision surface, to divide the data points of different categories in the vector space.
Hetal Bhavsar, Amit Ganatra
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SVM: Support Vector Machines

2009
Support vector machines (SVMs), including support vector classifier (SVC) and support vector regressor (SVR), are among the most robust and accurate methods in all well-known data mining algorithms. SVMs, which were originally developed by Vapnik in the 1990s [1-11], have a sound theoretical foundation rooted in statistical learning theory, require only
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