Learning Kernels for Support Vector Machines with Polynomial Powers of Sigmoid

Fernandes, Silas Evandro Nachif; Pilastri, André; Pereira, Luiz Haroldo; Pires, Rafael G.; Papa, João Paulo

doi:10.1109/sibgrapi.2014.36

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…However, to obtain a good distribution of the complex problem, a training set with a high number of instances is necessary. SVMbased classification uses kernel functions Linear, Radial Base Function kernel(RBF), Polynomial, and Sigmoid [29], [101].…”

Section: Support Vector Machinesmentioning

confidence: 99%

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

et al. 2022

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Internet traffic classification aims to identify the kind of Internet traffic. With the rise of traffic encryption and multi-layer data encapsulation, some classic classification methods have lost their strength. In an attempt to increase classification performance, Machine Learning (ML) strategies have gained the scientific community interest and have shown themselves promising in the future of traffic classification, mainly in the recognition of encrypted traffic. However, some of these methods have a high computational resource consumption, which make them unfeasible for classification of large traffic flows or in real-time. Methods using statistical analysis have been used to classify real-time traffic or large traffic flows, where the main objective is to find statistical differences among flows or find a pattern in traffic characteristics through statistical properties that allow traffic classification. The purpose of this work is to address statistical methods to classify Internet traffic that were little or unexplored in the literature. This work is not generally focused on discussing statistical methodology. It focuses on discussing statistical tools applied to Internet traffic classification Thus, we provide an overview on statistical distances and divergences previously used or with potential to be used in the classification of Internet traffic. Then, we review previous works about Internet traffic classification using statistical methods, namely Euclidean, Bhattacharyya, and Hellinger distances, Jensen-Shannon and Kullback-Leibler (KL) divergences, Support Vector Machines (SVM), Correlation Information (Pearson Correlation), Kolmogorov-Smirnov and Chi-Square tests, and Entropy. We also discuss some open issues and future research directions on Internet traffic classification using statistical methods.

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Section: Support Vector Machinesmentioning

confidence: 99%

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

et al. 2022

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“…) comprise the pair of input and output vectors, w is the weight factor, b is the threshold value, C is the penalty parameter, kernel function Φ is used to map input samples to higher space, ε is the epsilon parameter, and the upper and lower training errors are i ξ and i ξ * , respectively. For the kernel function, there are three typical kernel functions which are RBF, polynomial function and linear function [22][23][24]. These kernel functions are used to map nonlinear between the input and feature space.…”

Section: Support Vector Regressionmentioning

confidence: 99%

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

Zhan¹,

Li²

2016

Meas. Sci. Technol.

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A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant , the parameter gamma corresponding to RBF kernel and the epsilon parameter in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (, and ), respectively. The regression accuracy could be reflected by the coefficient of determination (). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.

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“…The sigmoid kernel is mostly known with the neural networks, but it is still a useful kernel that could be used with SVM to work out many applications [14,15]. The kernel function could be represented as follows:…”

Section: Sigmoid Kernelmentioning

confidence: 99%

“…Multi-scale spectral, size, shape, and texture information are used for classification. There are a low number of training samples for each class (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and a high number of classification features (148). the source of data is Institute for Global Environmental Strategies; 2108-11 Kamiyamaguchi, Hayama, Kanagawa, 240-0115 Japan [24].…”

Section: Data Setsmentioning

confidence: 99%

Single to Multiple Kernel Learning With Four Popular SVM Kernels (Survey)

Idris¹

2016

IJRET

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Machine learning applications and pattern recognition have gained great attention recently because of the variety of applications depend on machine learning techniques, these techniques could make many processes easier and also reduce the amount of human interference (more automation). This paper research four of the most popular kernels used with Support Vector Machines (SVM) for Classification purposes. This survey uses Linear, Polynomial, Gaussian and Sigmoid kernels, each in a single form and all together as un-weighted sum of kernels as form of Multi-Kernel Learning (MKL), with eleven datasets, these data sets are benchmark datasets with different types of features and different number of classes, so some will be used with Two-Classes Classification (Binary Classification) and some with Multi-Class Classification. Shogun machine learning Toolbox is used with Python programming language to perform the classification and also to handle the pre-classification operations like Feature Scaling (Normalization).The Cross Validation technique is used to find the best performance Out of the suggested different kernels' methods .To compare the final results two performance measuring techniques are used; classification accuracy and Area Under Receiver Operating Characteristic (ROC). General basics of SVM and used Kernels with classification parameters are given through the first part of the paper, then experimental details are explained in steps and after those steps, experimental results from the steps are given with final histograms represent the differences of the outputs' accuracies and the areas under ROC curves (AUC). Finally best methods obtained are applied on remote sensing data sets and the results are compared to a state of art work published in the field using the same set of data.

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Learning Kernels for Support Vector Machines with Polynomial Powers of Sigmoid

Cited by 5 publications

References 9 publications

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

Single to Multiple Kernel Learning With Four Popular SVM Kernels (Survey)

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