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Model Selection for Support Vector Machine Classification [PDF]
, 2002 We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed functional form of the kernel, model selection amounts to tuning kernel parameters and the slack penalty coefficient $C$.
Burges +22 more
core +4 more sources
Faster Support Vector Machines [PDF]
ACM Journal of Experimental Algorithmics, 2019 The time complexity of support vector machines (SVMs) prohibits training on huge datasets with millions of data points. Recently, multilevel approaches to train SVMs have been developed to allow for time-efficient training on huge datasets.
Sebastian Schlag +2 more
openaire +6 more sources
Solving Support Vector Machine with Many Examples
Journal of Telecommunications and Information Technology, 2023 Various methods of dealing with linear support vector machine (SVM) problems with a large number of examples are presented and compared. The author believes that some interesting conclusions from this critical analysis applies to many new optimization ...
Paweł Białoń
doaj +1 more source
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.
Cancer Genomics & Proteomics, 2018 Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping.
Shujun Huang +5 more
semanticscholar +1 more source
Support Vector Machine based Image Classification for Deaf and Mute People [PDF]
, 2018 A hand gesture recognition system provides a natural, innovative and modern way of nonverbal communication. It has a wide area of application in human computer interaction and sign language.
Godwin, M. J. (Mr) +3 more
core +1 more source
A Multiclass Nonparallel Parametric-Margin Support Vector Machine
Information, 2021 The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this
Shu-Wang Du +5 more
doaj +1 more source
Support vector machine for modelling and simulation of heat exchangers
, 2020 Manufacturing simulation is an encouraging field in every manufacturing industry. The manufacturing simulation facilitate to virtually analysis the performance of the product before manufacturing. So for most of the manufacturing activities are simulated
Sivaprakash Muthukrishnan +4 more
semanticscholar +1 more source
Support Vector Machine State Estimation
Applied Artificial Intelligence, 2019 The power system state estimator based on the support vector machine (SVM) and the weighted least squares (WLS) method is presented in the paper. The WLS provides state estimations necessary for creating SVM model which is then used for state estimation.
Vedran Kirinčić +3 more
doaj +1 more source
L2P-Norm Distance Twin Support Vector Machine
IEEE Access, 2017 A twin support vector machine (TWSVM) is an effective classifier, especially for binary data, which is defined by squared l2-norm distance in the objective function.
Xu Ma, Qiaolin Ye, He Yan
doaj +1 more source
Quantum-Enhanced Support Vector Machine for Sentiment Classification
IEEE Access, 2023 Quantum computers have potential computational abilities such as speeding up complex computations, parallelism by superpositions, and handling large data sets. Moreover, the field of natural language processing (NLP) is rapidly attracting researchers and
Fariska Zakhralativa Ruskanda +4 more
doaj +1 more source