首页|Researchers at Presidency University Have Reported New Data on Machine Learning (Pirap: a Study On Optimized Multi-language Classification and Text Categorizati on Using Supervised Hybrid Machine Learning Approaches)
Researchers at Presidency University Have Reported New Data on Machine Learning (Pirap: a Study On Optimized Multi-language Classification and Text Categorizati on Using Supervised Hybrid Machine Learning Approaches)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating in Bengaluru, India, by NewsRx journalists, research stated, "Nowadays, all the records in various langu ages are accessible with their advanced structures. For simple recovery of these digitized records, these reports should be ordered into a class as indicated by their content." The news reporters obtained a quote from the research from Presidency University , "Text Categorization is an area of Text Mining which helps to overcome this ch allenge. Text Classification is a demonstration of allotting classes to records. This paper investigates Text Classification works done in foreign Languages, re gional languages and a list of books' content. Messages available in different l anguages force the difficulties of NLP approaches. This study shows that supervi sed ML algorithms such as Logistic regression, Naive Bayes classifier, k-Nearest -Neighbor classifier, Decision Tree and SVMs performed better for Text Classific ation tasks. The automated document classification technique is useful in our da y-to-day life to find out the type of language and different department books ba sed on their text content. We have been using different foreign and regional lan guages here to classify such as Tamil, Telugu, Kannada, Bengali, English, Spanis h, French, Russian and German. Here, we utilize one versus all SVMs for multi-ch aracterization with 3-crease Cross Validation in all cases and see that SVMs out perform different classifiers."
BengaluruIndiaAsiaCyborgsEmergin g TechnologiesMachine LearningPresidency University
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