Autor/Autorin: Sharma Kapil
Dr. Kapil Sharma is a associate professor at the Delhi Technological University. He is an expert in software engineering, reliability, and swarm intelligence, He is active IEEE member. He has published many research papers and books.
Spam or unsolicited email has become a major problem for companies and private users. The problems associated with spam and various approaches that attempt to deal with it, have been presented here. Statistical classifiers are one such group of methods that show adequate performance in filtering spam, based upon the previous knowledge gathered through collected and classified emails. Learning algorithms that uses the Naive Bayesian classifier have shown promising results in separating spam from legitimate mail. An encoded and fragmented database approach that resembles radix sort technique has been proposed and applied for first time to improve Paul Graham's Naive Bayes machine learning algorithm for spam filtering.
Spam or unsolicited email has become a major problem for companies and private users. The problems associated with spam and various approaches that attempt to deal with it, have been presented here. Statistical classifiers are one such group of methods that show adequate performance in filtering spam, based upon the previous knowledge gathered through collected and classified emails. Learning algorithms that uses the Naive Bayesian classifier have shown promising results in separating spam from legitimate mail. An encoded and fragmented database approach that resembles radix sort technique has been proposed and applied for first time to improve Paul Graham's Naive Bayes machine learning algorithm for spam filtering.
Über den Autor
Dr. Kapil Sharma is a associate professor at the Delhi Technological University. He is an expert in software engineering, reliability, and swarm intelligence, He is active IEEE member. He has published many research papers and books.
Klappentext
Spam or unsolicited email has become a major problem for companies and private users. The problems associated with spam and various approaches that attempt to deal with it, have been presented here. Statistical classifiers are one such group of methods that show adequate performance in filtering spam, based upon the previous knowledge gathered through collected and classified emails. Learning algorithms that uses the Naive Bayesian classifier have shown promising results in separating spam from legitimate mail. An encoded and fragmented database approach that resembles radix sort technique has been proposed and applied for first time to improve Paul Graham's Naive Bayes machine learning algorithm for spam filtering.
