Support vector machine

2.4 Support Vector Machine (SVM) The support vector machines are supervised learning models, derived from statistical learning theory (Vapnik 1995) that analyze data and recognize pattern. SVM effectively perform non-linear classification by using kernel functions, implicitly mapping their inputs into high-dimensional spaces. This makes it a suitable tool in predicting the compressive strength of concrete which is non-linearly related to its mix ingredients. In the SVM model, the training data is

includes the learning and study of Support Vector Machine and its various different variations. The task of Support Vector Machine map data to a higher dimensional space and helps to find out the maximal marginal hyperplane to separate the data. In this paper, a learning method, Support vector Machine, is applied on the different datasets for getting more enhanced results. SVM is introduced in the early 90’s, and they led to an explosion of interest in machine learning. SVM have been developed by

PSVM: Parallelizing Support Vector Machines on Distributed Computers Edward Y. Chang∗, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, & Hang Cui Google Research, Beijing, China Abstract Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads

Parallel Support Vector Machine Junfeng Wu Junming Chen May 6, 2016 1 INTRODUCTION Support vector machines is a supervised machine learning alogrithom used for classification. The problem could be written : minimize 1 |w |2 2 yi((w,xi)+b)−1≥0 where w is a linear combination of the training data: n w = αi k(xi ) i=1 this could be further written in a dual form[5]: min 1αTQα−eTα α2 0≤αi ≤C, yTα=0, ∀i ≤n where Q is the kernel matrix. This dual form is a quadratic programming problem with linear

Optimization Technique for Feature Selection and Classification Using Support Vector Machine Abstract— Classification problems often have a large number of features in the data sets, but only some of them are useful for classification. Data Mining Performance gets reduced by Irrelevant and redundant features. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main objectives

Multi-feature advanced support vector machine method for classification of polarimetric synthetic aperture radar data Purnima Arora1, Dr.Paras Chawla2, Gaurav Malik3 1,2,3Electronics & Communication Engineering, Seth Jai Parkash Mukand Lal Institute of Engineering & Technology, Radaur, Yamunanagar, Haryana, India-135133, E-mail: 1purnima5142@jmit.ac.in; paraschawla@jmit.ac.in2; 3gauravmalikece@gmail.com Abstract— Support Vector Machine (SVM) is regarded as a good alternative of the traditional

Support Vector Machine (SVM) is primarily a classier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables. For categorical variables a dummy variable is created with case values as either 0 or 1. Thus, a categorical dependent variable consisting of three levels, say (A, B, C), is represented

Improved the Detection Ratio of Cyber Attack Using Feature Reduction Based on Support Vector Machine and Glowworm Optimization Abstract— The swarm intelligence plays vital role in feature reduction process in cyber-attack detection. The family of swarm intelligence gives bucket of algorithm for the processing of feature reduction such as ant colony optimization, particle swarm optimization and many more. In family of swarm new algorithm is called glowworm optimization algorithm based on the concept

and attempts an approach for classification of brain images to search for pathology and normality part of brain by extracting salient features of input brain image and the region of interest is identified using kernel k-means algorithm. A support vector machine (SVM) a supervised learning process is used for classification of AD, which is recognized on basis of blue color is normal brain part and red color is pathology related. I. INTRODUCTION Neurodegenerative diseases affect central nervous

paper, an approach for gender classification is carried out combining frontal face images, Haar cascades, Histogram of Oriented Gradients and Support Vector Machines. The comparison of the existing methods that delves into the effects of Haar Cascade Classifier and Histogram of Oriented Gradients(HOG) for Face Detection and the use of Support Vector Machines(SVM) for Gender Classification. A database of 2-D facial images was used, consisting of individual as well as group photographs. These images