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Chunking with support vector machines [PDF]
Second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies 2001 - NAACL '01, 2001 We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted
TAKU KUDO, YUJI MATSUMOTO
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Support Vector Machines inR [PDF]
Journal of Statistical Software, 2006 Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.
Karatzoglou, Alexandros +2 more
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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
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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
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MRI brain classification using support vector machine [PDF]
, 2011 The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images ...
Abdullah, N. B. +2 more
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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
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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
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Support Vector Machines with Applications
Statistical Science, 2006 Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik's statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often providing improved results compared with other techniques.
Moguerza, Javier M., Muñoz, Alberto
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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
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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
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