Sparse Bayesian Model Based On Text Classfication

Text classification is automatically signed and classified according to certain classification standards.This intelligent classification makes us be able to know whether the subsequent text is needed by us without the expression of text and other information.Text classification generally includes text expression,categorization classifier selection,evaluation and feedback of classification results,etc.With the development of network technology,automatic text became the industry pioneer and various intelligent and personalized search engines,and it takes the lead in many fields.The thesis not only refined partial feature extraction but also made a detailed explanation for various algorithms and some basic concepts and the selection of classifier,and analyzed the problem of text categorization and explained the advantages and disadvantages and how to better apply them.Secondly,aiming at the disadvantages of various algorithms,it put forward the Sparse Bayesian Probability Model,so as to make it be able to better adapt to the needs of text categorization and improve the relevant technologies.Thirdly,through judging the class density and related properties of contraction factor,it gave the necessary proof and explanation.Sparse Bayesian Probability Model greatly promoted the accuracy of text categorization and greatly reduced the manpower cost.We also used the statistical learning approach to effectively classify the classifier.Finally,the advantages and disadvantages of all kinds of classifiers were evaluated,and the model's purpose and the corresponding limits were also indicated.Nowadays,statistical methods have become a main method and clear standard in the field of text classification,and such an application is more handy.The Sparse Bayesian Model adopted by us not only reduced the calculated amount of text categorization,but also improved the speed of text classification.Our experimental results show that the Sparse Bayesian Model is superior to the algorithms of traditional models in terms of big data,which not only effectively improved the duplicate checking rate and classification speed,but also better implemented the object-oriented accuracy.