Naive Bayesian Classifier Seminar

For more info : Naive Bayesian Classifier

***Peeking Into The Seminar***

Classifying is the basic feature of all machine learning. The most famous and widely known way of classifying is called Naive Bayesian. Naive Bayesian Classifier processes learn on the basis of Bayes’s theorem and hypothesis that all features are independent. Users can test its performance by pre-processing the learning data and inputting it into the Naive Bayesian classifier. Advantages that the Naive Bayesian classifier has over other classifiers are

1) It is very time-saving

2) It has low storage requirements

3) It shows a great performance

Thus, in the academic and business world, people should use and consider this as the standard for classifying texts. More information and explanations are in the powerpoint material attached. Lastly, these days, Deep Belief Network is widely used in the field of deep learning in conjunction with the Naive Bayesian classifier.