Parallel Support Vector Machines Is A Supervised Machine Learning Alogrithom Used For Classification
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 constraints. A solver to this problem like SMO or IPM reuquires a time complexity of O(n3) and space complexity of O(n2), which makes SVM hard to scale. In our final project we implemented two parallel quadratic programming solvers for SVM and implemented a matrix factorization algorithm to improve the performance. Later we evaluated our implementation.
1
i =1,...,l,
2 SMO SOLVER IN PARALLEL
SMO is one of the most common ways to solve quadratic programming problem. SMO is a iterative alogrithm. In each iteration, the alogorithm optimize one pair of Lagrange multipliers (α1,α2) that could best accelerate the convergence util the lagrange multipliers meet the convergence condition.
2.1 SELECTING AND UPDATING α PAIR
We select alpha1 and alpha2 that make the largest progress towards the global maximum value on each side of the hyper plane according to the heuristic function. The heuristic function is as follow: n f (i) = aj yj k(xj ,xi )− yi j=1 The selection of α pair is as…

The Support Vector Machine ( Svm )
1426 Words  6 Pages2.4 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 nonlinear classification by using kernel functions, implicitly mapping their inputs into highdimensional spaces. This makes it a suitable tool in predicting the compressive strength of concrete which is nonlinearly related to its mix ingredients. In the SVM model, the training data is…

Support Vector Machine ( Svm )
767 Words  4 PagesSupport 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…

Dynamic News Classification Using Machine Learning
2198 Words  9 PagesDynamic News Classification using Machine Learning Introduction Why this classification is needed ? (Ashutosh) The exponential growth of the data may lead us to a time in future where huge amount of data would not be able to be managed easily. Text Classification is done through Text Mining study which would help sorting the important texts from the content or a document to manage the data or information easily. //Give a scenario, where classification would be mandatory. Advantages of classification…

Support Vector Machines On Distributed Computers
1452 Words  6 PagesPSVM: 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 rowbased, approximate matrix factorization, and which loads…

Multi Features Advanced Support Vector Machine Method For Classification Of Polarimetric Synthetic Aperture Radar Data
3852 Words  16 Pagesadvanced 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, India135133, Email: 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 learning classification…

A Machine Learning Approach For Emotions Classification
1388 Words  6 PagesA machine learning approach for emotions classification in Micro blogs ABSTRACT Micro blogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life every day. Therefore micro blogging websites are rich sources of data for opinion mining and sentiment analysis. Because micro blogging has appeared relatively recently, there are a few research works that are devoted to this topic.In this paper, we are focusing on using…

An Enhanced Approach For Web Services Clustering Using Supervised Machine Learning Techniques
1698 Words  7 PagesAn Enhanced Approach for Web Services Clustering using Supervised Machine Learning Techniques ABSTRACT Automatic document classification provides techniques that may improve and support web service clustering. As the number of services increases, the cost of classifying services through manual work increases. In this research, we presented an enhanced approach for service clustering that combines text mining and machine learning technology. The method only uses text description of each service…

The Application Of Machine Learning
1253 Words  6 Pagesrecent years, machine learning has made very significant leaps in terms of development. It has undergone a lot of improvement, growth in the industry. Because of its ability to learn and improve itself and make predictions based on data, its popularity has grown leaps and bounds in the recent years mainly due to the large scale data processing and managing capacities of machines nowadays. Many applications of machine learning has come into picture in the recent years. Machine Learning makes use various…

Machine Learning
2512 Words  11 Pagesthey must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial…

Optimization Technique For Feature Selection And Classification Using Support Vector Machine
2540 Words  11 PagesSelection 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 are maximizing the classification performance…
More about Parallel Support Vector Machines Is A Supervised Machine Learning Alogrithom Used For Classification

The Support Vector Machine ( Svm )
1426 Words  6 Pages 
Support Vector Machine ( Svm )
767 Words  4 Pages 
Dynamic News Classification Using Machine Learning
2198 Words  9 Pages 
Support Vector Machines On Distributed Computers
1452 Words  6 Pages 
Multi Features Advanced Support Vector Machine Method For Classification Of Polarimetric Synthetic Aperture Radar Data
3852 Words  16 Pages 
A Machine Learning Approach For Emotions Classification
1388 Words  6 Pages 
An Enhanced Approach For Web Services Clustering Using Supervised Machine Learning Techniques
1698 Words  7 Pages 
The Application Of Machine Learning
1253 Words  6 Pages 
Machine Learning
2512 Words  11 Pages 
Optimization Technique For Feature Selection And Classification Using Support Vector Machine
2540 Words  11 Pages