Applications of Artificial Intelligence in Machine Learning: Review and Prospect
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TLDR
A brief review and future prospect of the vast applications of machine learning has been made.Abstract:
Machine learning is one of the most exciting recent technologies in Artificial Intelligence. Learning algorithms in many applications that’s we make use of daily. Every time a web search engine like Google or Bing is used to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages. Every time Facebook is used and it recognizes friends' photos, that's also machine learning. Spam filters in email saves the user from having to wade through tons of spam email, that's also a learning algorithm. In this paper, a brief review and future prospect of the vast applications of machine learning has been made.read more
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TL;DR: This survey discusses the main approaches to text categorization that fall within the machine learning paradigm and discusses in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
Thumbs up? Sentiment Classiflcation using Machine Learning Techniques
TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Proceedings ArticleDOI
Thumbs up? Sentiment Classification using Machine Learning Techniques
TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Journal ArticleDOI
Machine learning for medical diagnosis: history, state of the art and perspective
TL;DR: An overview of the development of intelligent data analysis in medicine from a machine learning perspective: a historical view, a state-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence.