Evaluating the Performance of an Admissible Kernel Function in Banach Space for Binary Data

dc.categoryJournal Article
dc.contributor.authorKalpana, B
dc.date.accessioned2017-03-28T23:32:42Z
dc.date.available2017-03-28T23:32:42Z
dc.date.issued2013
dc.departmentComputer Scienceen_US
dc.description.abstractClassification is a learning function that maps a given data item into one of several predefined classes. H is a data analysis technique that extracts models describing important data classes and predicts future values. Basically, classification techniques have better capability to handle a wider variety o f datasets than regression. It can also be described as a supervised learning algorithm in the machine learning process. Support Vector Machine (SVM) is an emerging classifier based on supervised machine learning approach. It is originally used to symbolize popular and modern classifiers that have a well-defined theoretical foundation to provide some enviable performances /IJ. In this paper, an admissible kernel function in Banach Space is proposed as an optima! kernel function for real time applications. The experimental results are carried out using benchmark binary datasets which are taken from UCI Machine Learning Repository and their performance are evaluated using various measures like support vector, support vector percentage, training time and accuracy.en_US
dc.identifier.urihttps://ir.avinuty.ac.in/handle/avu/2257
dc.langEnglishen_US
dc.publisher.nameInternational Journal of Advanced Computer Researchen_US
dc.publisher.typeInternationalen_US
dc.titleEvaluating the Performance of an Admissible Kernel Function in Banach Space for Binary Dataen_US
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