scholarly journals On Bayesian Inference, Maximum Entropy and Support Vector Machines Methods

2006 ◽  
Author(s):  
Mihai Costache ◽  
Marie Liénou ◽  
Mihai Datcu
Keyword(s):  
Bayesian Inference ◽  
Support Vector Machines ◽  
Maximum Entropy ◽  
Support Vector ◽  
Vector Machines

scholarly journals Advanced Machine Learning Techniques used for Deodorant Based Sentimental Anatomy

2019 ◽  
Vol 8 (4) ◽  
pp. 5813-5816
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Maximum Entropy ◽  
Source Material ◽  
Unstructured Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Learning Techniques ◽  
Vector Machines

Now a days there is lots of data floating in the life of world access i.e Internet which is unstructured data.To manage this unstructured data we are introduced some classification algorithms in machine learning to classify the data.Sentiment Analysis[5] is contextual mining of text from documents ,reviews of customers which distinguishes and concentrates emotional data in source material. Assessment API works in fourteen unique dialects .We consider the issue of grouping records not by subject, however by generally speaking slant, e.g., deciding if an audit is certain or negative. Utilizing antiperspirants surveys as information, we locate that standard AI systems absolutely beat human-delivered baselines. The AI stratagies we connected with for arrangement are Naive Bayes, maximum entropy[2] classification, and support vector machines classification algorithms for sentiment classification as on traditional topic-based categorization.[1].

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