Comparison of Naive Bayes, Back Propagation, And Deep Learning algorithm to Measure the Performance Using Datasets

Volume 11 Issue 2 October - December 2016

Research Paper

Comparison of Naive Bayes, Back Propagation, And Deep Learning algorithm to Measure the Performance Using Datasets

J. Sharmila*

Assistant Professor, Department of Computer Science, Bharathidasan University Constituent College for Women, TamilNadu, India.

Sharmila, J. (2016). Comparison of Naive Bayes, Back Propagation, Deep Learning algorithm to Measure the Performance Using Datasets. i-manager’s Journal on Software Engineering, 11(2), 1-12. https://doi.org/10.26634/jse.11.2.13443

Abstract

Mining the possible information from the web is one of the popular research topics. This paper comprises Naive Bayes, Back Propagation, and Deep Learning algorithm to describe various ranking scores on the training data set. To construct the extraction model, the authors have proposed a new algorithm for training support vector machines, known as Deep Learning. It requires the solution of a very large Quadratic Programming (QP) optimization problem. Naive Bayes for probabilistic model and back propagation algorithm were evaluated, and the results are compared and proved in an efficient manner. They even exploited various data sets of dissimilar entities which are used to measure the strength of association between words and co-occurrence statistics are also computed from this computation.