Results 261 to 270 of about 9,169,616 (322)

MS-SVM: Minimally Spanned Support Vector Machine

Applied Soft Computing Journal, 2018
Abstract For a Support Vector Machine (SVM) algorithm, the time required for classifying an unknown data point is proportional to the number of support vectors. For some real time applications, use of SVM could be a problem if the number of support vectors is high.
Nikhil Ranjan Pal
exaly   +3 more sources

Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)

International Journal of River Basin Management, 2019
The current calculations of water quality index (WQI) were sometimes can be very complex and time-consuming which involves sub-index calculation like BOD and COD, however with the support vector ma...
Wei Cong Leong, Alireza Bahadori, Jie Zhang, Z. Ahmad   +3 more
openaire   +3 more sources

Vote Parallel SVM: An Extension of Parallel Support Vector Machine

2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2018
Support Vector Machine (SVM) is a set of machine learning algorithms, which has been widely used in diverse domains. With the increasing size of datasets, the traditional SVM training algorithms for large-scale datasets become infeasible. Mathematical optimization and cascade parallelism are both popular strategies for accelerating SVM training.
Qun Jin, Ke Yan, Huijuan Lu
exaly   +2 more sources

If-SVM: Iterative factoring support vector machine

Multimedia Tools and Applications, 2020
Support Vector Machine (SVM) is widely applied in classification and regression tasks where support vectors are pursued through convex quadratic programming technique due to its effectiveness and efficiency. However, existing studies ignore the importance of training samples when they are fed into the model.
Yuqing Pan, Wenpeng Zhai, Wei Gao, Xiangjun Shen   +3 more
openaire   +1 more source

Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

Engineering applications of artificial intelligence, 2021
The advance rate (AR) of a tunnel boring machine (TBM) in hard rock condition is a key parameter for the successful accomplishment of a tunneling project, and the proper and reliable prediction of this parameter can lead to minimizing the risks ...
Jian Zhou, Y. Qiu, Shuangli Zhu, D. J. Armaghani, Chuanqi Li, Hoang Nguyen, S. Yagiz   +6 more
semanticscholar   +1 more source

Road Crack Detection using Support Vector Machine (SVM) and OTSU Algorithm

2019 6th International Conference on Electric Vehicular Technology (ICEVT), 2019
Cracks are one type of pavement surface damages, whose assessment is very important for developing road network maintenance strategies, which aims to ensure the functioning of the road and driving safety.
Y. Sari, P. Prakoso, A. R. Baskara
semanticscholar   +1 more source

Support Vector Machine Algorithm in Machine Learning

2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), 2022
The Support Vector methods was proposed by V.Vapnik in 1965, when he was trying to solve problems in pattern recognition. In 1971, Kimeldorf proposed a method of constructing kernel space based on support vectors.
Qiyu Wang
semanticscholar   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +2 more sources