Results 61 to 70 of about 725,482 (185)

General Vector Machine [PDF]

arXiv, 2016
The support vector machine (SVM) is an important class of learning machines for function approach, pattern recognition, and time-serious prediction, etc. It maps samples into the feature space by so-called support vectors of selected samples, and then feature vectors are separated by maximum margin hyperplane.
arxiv  

Support vector machine to criminal recidivism prediction [PDF]

International Journal of Electronics and Telecommunications
Internal security of the state is one of the prerequisites for sustainable development. To ensure the public safety and personal security of citizens, it is necessary to develop effective measures to reduce crime and prevent crime in the future.
Olha Kovalchuk, Ruslan Shevchuk, Ludmila Babala, Mykhailo Kasianchuk   +3 more
doaj   +1 more source

Rgtsvm: Support Vector Machines on a GPU in R [PDF]

arXiv, 2017
Rgtsvm provides a fast and flexible support vector machine (SVM) implementation for the R language. The distinguishing feature of Rgtsvm is that support vector classification and support vector regression tasks are implemented on a graphical processing unit (GPU), allowing the libraries to scale to millions of examples with >100-fold improvement in ...
arxiv  

Forecasting of Short-term Power Load Based on Improved PSO Algorithm and LS-SVM

Gong-kuang zidonghua, 2012
For problems of small samples, nonlinear, high dimensions and the local minimum of electric power load, a modeling method based on the least square support vector machine was proposed to forecast short-term power load by taking historical load ...
PAN Lei, LI Li-juan, DING Ting-ting, LIU Dui   +3 more
doaj  

The Study of Information Security Risk Assessment Based on Support Vector Machine

MATEC Web of Conferences, 2018
This paper establishes an information security evaluation model based on support vector machine by analyzing the factors affecting information security risk assessment.
Xie Haiyan, Wang Ying, Zhang Xianghong
doaj   +1 more source

LINEX Support Vector Machine for Large-Scale Classification

IEEE Access, 2019
Traditional soft margin support vector machine usually uses hinge loss to build a classifier with the “maximum-margin” principle. However, C-SVM depends on support vectors causing the loss of data information.
Yue Ma, Qin Zhang, Dewei Li, Yingjie Tian   +3 more
doaj   +1 more source

Support vector machines/relevance vector machine for remote sensing classification: A review [PDF]

Proceeding of the Workshop on Application of advanced soft computing Techniques in Geo-spatial Data Analysis. Department of Civil Engineering, IIT Bombay, Sept. 22-23,2008, 211-227, 2011
Kernel-based machine learning algorithms are based on mapping data from the original input feature space to a kernel feature space of higher dimensionality to solve a linear problem in that space. Over the last decade, kernel based classification and regression approaches such as support vector machines have widely been used in remote sensing as well ...
arxiv  

Tverberg's theorem and multi-class support vector machines [PDF]

arXiv
We show how, using linear-algebraic tools developed to prove Tverberg's theorem in combinatorial geometry, we can design new models of multi-class support vector machines (SVMs). These supervised learning protocols require fewer conditions to classify sets of points, and can be computed using existing binary SVM algorithms in higher-dimensional spaces,
arxiv  

Security risk prediction technology for power monitoring system under the integration of OT and IT

International Journal for Simulation and Multidisciplinary Design Optimization
As an essential force for economic advancement and social stability, the security of the power system has always been a concern. Therefore, the security risks of power monitoring systems are a research focus.
Zhu Zhennan, Jin Jingquan
doaj   +1 more source

Exotic and physics-informed support vector machines for high energy physics [PDF]

arXiv
In this article, we explore machine learning techniques using support vector machines with two novel approaches: exotic and physics-informed support vector machines. Exotic support vector machines employ unconventional techniques such as genetic algorithms and boosting.
arxiv