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In this paper we discuss the techniques involved in the design of the famous statistical spam filters that include Naive Bayes, Term Frequency-Inverse Document Frequency, K-Nearest Neighbor, Support Vector Machine, and Bayes Additive Regression Tree. We compare these techniques with each other in terms of accuracy, recall, precision, etc. Further, we discuss the effectiveness and limitations of statistical filters in filtering out various types of spam from legitimate e-mails.
Selective on-chip optical filters with high rejection levels are key components for a wide range of advanced photonic circuits. However, maximum achievable rejection in state-of-the-art on-chip devices is seriously limited by phase errors arising fro
Standard autoregressive language models perform only polynomial-time computation to compute the probability of the next symbol. While this is attractive, it means they cannot model distributions whose next-symbol probability is hard to compute. Indee
The high cost of acquiring labels is one of the main challenges in deploying supervised machine learning algorithms. Active learning is a promising approach to control the learning process and address the difficulties of data labeling by selecting la
Using powerful posterior distributions is a popular approach to achieving better variational inference. However, recent works showed that the aggregated posterior may fail to match unit Gaussian prior, thus learning the prior becomes an alternative w
In this paper an attempt is made to review technological, economical and legal aspects of the spam in detail. The technical details will include different techniques of spam control e.g., filtering techniques, Genetic Algorithm, Memory Based Classifi