Results 1 to 10 of about 263,437 (264)
Wavelet Support Vector Machine [PDF]
IEEE Transactions on Systems, Man, and Cybernetics, 2004 An admissible support vector (SV) kernel (the wavelet kernel), by which we can construct a wavelet support vector machine (SVM), is presented. The wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. The existence of wavelet kernels is proven by results of theoretic analysis.
Li Zhang, Licheng Jiao, Weida Zhou
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Functional support vector machine. [PDF]
BiostatisticsAbstract Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the ...
Xie S, Ogden RT.
europepmc +3 more sources
On Coresets for Support Vector Machines [PDF]
Theoretical Computer Science, 2020 We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the original data set. Since the size of the
Murad Tukan +3 more
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Global-local least-squares support vector machine (GLocal-LS-SVM)
PLoS ONE, 2023 This study introduces the global-local least-squares support vector machine (GLocal-LS-SVM), a novel machine learning algorithm that combines the strengths of localised and global learning.
Ahmed Youssef Ali Amer
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The Stata Journal: Promoting communications on statistics and Stata, 2016 Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines.
Guenther, Nick, Schonlau, Matthias
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Safe transductive support vector machine
Connection Science, 2022 Since semi-supervised learning can use fewer labelled samples to train a better model, semi-supervised methods are becoming popular in data mining. As an important algorithm of semi-supervised support vector machines (S $ ^{3} $ VM), transductive support
Haiyan Chen +3 more
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Efficient heart disease diagnosis based on twin support vector machine
Diagnostyka, 2021 Heart disease is the leading cause of death in the world according to the World Health Organization (WHO). Researchers are more interested in using machine learning techniques to help medical staff diagnose or detect heart disease early.
Youcef Brik +2 more
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Properties of Support Vector Machines [PDF]
Neural Computation, 1998 Support vector machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed support vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of ...
PONTIL M, VERRI, ALESSANDRO
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Support Vector Machine For Hoax Detection
SINTECH (Science and Information Technology) Journal, 2023 Along with the development of information technology, news media has also developed by presenting information online Along with the rapid development of online news, the spread of fake news information (hoaxes) is also increasing rapidly and widely ...
Ni Wayan Sumartini Saraswati +3 more
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Nested support vector machines [PDF]
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 The one-class and cost-sensitive support vector machines (SVMs) are state-of-the-art machine learning methods for estimating density level sets and solving weighted classification problems, respectively. However, the solutions of these SVMs do not necessarily produce set estimates that are nested as the parameters controlling the density level or cost ...
Gyemin Lee, Clayton Scott
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