The support vector machine under test

International Journal of Advanced and Applied Sciences, 2018

Classification is a challenging problem in the various fields of knowledge i.e., Pattern Recognition, Data Mining, Knowledge Discovery from Database etc. There is various classification methods are proposed in the contemporary literature. The choice of an appropriate classifier to achieve the optimal performance on a specific problem needs more empirical studies. There are various algorithmic paradigms like, Associative Classification; Decision Trees based classification, Statistical Classification and Support Vector Machines etc. which are exploited for the classification purposes. This paper investigates the performance of Support Vector Machine (SVM) based classifiers namely SMO-C, C-SVM-C, and NU-SVM-C. SVM is a very successful classification approach for the binary classification as well as non-binary classification problems. This study, performance comparative analysis of SVM based classification approach on public data sets; exploit the implementation of the corresponding classifiers in the KEEL. The SVM-C approach wins one time, draw 5 times and lost 6 times with respective other approaches. The NU_SVM-C win one time, draw 4 times and lost 7 times while SMO-C wins 5 times, draw 3 times and loss 4 times. It is shown that the performance of SMO-C is promising with respect to other SVM based classifiers.

2003

Support vector machine (SVM) is one of the recent methods for statistical learning, it addresses classification and regression problems. It can be considered as an alternative to neural networks. The advantage of SVM, with respect to neural network, is that it provides a theoretical framework for taking into account not only the experimental data to design an optimal classifier, but also a structural behavior for allowing better generalization capability. This paper introduces SVM theory, applications and its algorithmic implementations. Although there are proven algorithms for constructing SVM programs, it is usually faster and also more reliable to make use or adapt a public domain SVM implementation packages. In this paper, we explain three of the popularly used C/C++ based SVM packages and demonstrate their usage. We report some results of their usage in classification on a number of different datasets, taking into consideration the tuning of SVM kernel hyperparameters for performance measures.

Kernel methods and support vector machines have become the most popular learning from examples paradigms. Several areas of application research make use of SVM approaches as for instance hand written character recognition, text categorization, face detection, pharmaceutical data analysis and drug design. Also, adapted SVM’s have been proposed for time series forecasting and in computational neuroscience as a tool for detection of symmetry when eye movement is connected with attention and visual perception. The aim of the paper is to investigate the potential of SVM’s in solving classification and regression tasks as well as to analyze the computational complexity corresponding to different methodologies aiming to solve a series of afferent arising sub-problems.

Predication of terrorist groups that responsible of terrorist attacks is a challenging task and a promising research area. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. The main objective of this research is to conduct a detailed comparative study among Support Vector Machine as one of the successful prediction classifiers that proved highly performance and other supervised machine learning classification and hybrid classification algorithms. Whereas most promising methods are based on support vector machine (SVM); so there is a need for a comprehensive analysis on prediction accuracy of supervised machine learning algorithms on different experimental conditions, and hence in this research we compare predictive accuracy and comprehensibility of explicit, implicit, and hybrid machine learning models and algorithms. This research based on predicting terrorist groups responsible of attacks in Middle East & North Africa from year 2004 up to 2008 by comparing various standard, ensemble, hybrid, and hybrid ensemble machine learning methods and focusing on SVM. The compared classifiers are categorized into main four types namely; Standard Classifiers, Hybrid Classifiers, Ensemble Classifiers, and Hybrid Ensemble Classifiers. In our study we conduct three different experiments on the used real data, afterwards we compare the obtained results according to four different performance measures. Experiments were carried out using real world data represented by Global terrorism Database (GTD) from National Consortium for the study of terrorism and Responses of Terrorism (START).

2021

A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments. 1. Motivation and Goal SVMs are state-of-the-art machine learning techniques with their root in structural risk minimization [57, 58]. Additionally, SVM...

Wiley Interdisciplinary Reviews: Computational Statistics, 2009

Support vector machines (SVMs) are a family of machine learning methods, originally introduced for the problem of classification and later generalized to various other situations. They are based on principles of statistical learning theory and convex optimization, and are currently used in various domains of application, including bioinformatics, text categorization, and computer vision.  2009 John Wiley & Sons, Inc. WIREs Comp Stat 2009 1 283-289 S upport vector machines (SVMs), introduced by

2011

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2004

Support vector machines (SVMs) are powerful tools for providing solutions to classifcation and function approximatiun problem. In this paper, the comparison among the four cMication methods is conducted. The four methods BIP Lagrangian Support Vector Machine (LSVM), Finite Newton Lagrangian Support Vector Machine (NLSVM), Smooth Support Vector Machine (SSVM) and Finite Newton Support Vector Machine (NSVM). The comparison of their Algorithm in generating a linear or nonlinear kernel classifier, accuracy and computational complexity is also given. The study provides some guidelines for choosing an appropriate one from four SVM classification methods in a classification problem.

Wiley Encyclopedia of Operations Research and Management Science, 2010

2008

This document has been written in an attempt to make the Support Vector Machines (SVM), initially conceived of by Cortes and Vapnik [1], as sim-ple to understand as possible for those with minimal experience of Machine Learning. It assumes basic mathematical knowledge in areas ...