A Bayesian Approach to Filtering Junk E-Mail

By Mehran Sahami, Susan T. Dumais, David E. Heckerman, Eric Horvitz. 1998

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function (2019, background): “It has a wide range of applications such as sentiment analysis (Pang and Lee 2008), topic categorization (Lewis et al. 2004), and email ﬁltering (Sahami et al. 1998).”
Hierarchical Attention Networks for Document Classification (2016, background): “It has broad applications including topic labeling (Wang and Manning, 2012), sentiment classiﬁcation (Maas et al., 2011; Pang and Lee, 2008), and spam detection (Sahami et al., 1998).”
Is Feature Selection Secure against Training Data Poisoning? (2015, methodology): “Similarly, text classifiers for spam filtering rely on the construction of a large dictionary to identify words that are mostly discriminant of spam and legitimate emails (Sahami et al., 1998; McCallum & Nigam, 1998; Graham, 2002; Robinson, 2003).”
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks (2014, background): “…with different types of documents and categories, such as topic categorization to detect discussed topics (e.g., sports, politics), spam detection (Sahami et al., 1998), and sentiment classiﬁcation (Pang et al., 2002; Pang and Lee, 2008; Maas et al., 2011) to determine the sentiment typically in…”
A comparison of event models for naive bayes text classification (1998, methodology): “When adding non-text features to the classifier, (such as whether or not an email message has more than one recipient), such features can be included exactly as the word features are when using the multi-variate Bernoulli model (Sahami et al. 1998).”
Words versus Character n-Grams for Anti-Spam Filtering (2007, unknown): “. 3 , , . 3 ( 2 ) In this study, we only consider 3-grams, 4-grams, and 5-grams as candidate n-grams.”
Spam Filtering with Naive Bayes - Which Naive Bayes? (2006, methodology): “[21] used a nb classifier with a multi-variate Bernoulli model (this is also the model we had used in [1]), a form of nb that relies on Boolean attributes, whereas Pantel and Lin [19] in effect adopted the multinomial form of nb, which normally takes into account term frequencies.”
Review: machine learning techniques applied to cybersecurity (2019, methodology): “Two are the main algorithms: Sahami et al. [75] set the basis of spam filtering with Bayesian classifiers back in 1998; later, Graham [76] improved the classifier selecting the most relevant features online before performing the classification.”
Spam Filtering Based On The Analysis Of Text Information Embedded Into Images (2006, methodology): “This has lead in recent years to investigate the use of text categorisation techniques based on the machine learning and pattern recognition approaches for e-mail semantic content analysis (see for instance Sahami et al., 1998; Drucker et al., 1999; Graham, 2002; Zhang et al., 2004).”
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter (2011, methodology): “Others are spam email analysis based on data compression algorithms (Bratko et al. 2006), machine learning (Goodman , Heckerman, and Rounthwaite 2005; Sahami et al. 1998) or statistics (Fetterly, Manasse, and Najork 2004; Ntoulas et al. 2006; Yoshida et al. 2004).”
Leave a Reply: An Analysis of Weblog Comments (2006, unknown): “, [15]).”
Good Word Attacks on Statistical Spam Filters (2005, methodology): “Naive Bayes was first applied to spam filtering by ( Sahami et al., 1998 ).”
A survey and experimental evaluation of image spam filtering techniques (2011, methodology): “…text categorisation techniques based on machine learning have been widely investigated by several authors, to implement textual content analysis (Sahami et al., 1998; Drucker et al., 1999; Androutsopoulos et al., 2000; Graham, 2002a; Robinson, 2003; Meyer and Whateley, 2004; Koprinska et al.,…”
An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages (2000, background): “It is, therefore, desirable to develop anti-spam filters that will learn automatically how to block spam messages by processing previously received spam and legitimate messages.”
Tracking recurring contexts using ensemble classifiers: an application to email filtering (2009, methodology): “Naive Bayes was chosen mainly because it is inherently incremental (class priors and feature probability distributions can be calculated incrementally) and has been widely used in text classification tasks in general [34,35] and in email classification in particular [ 36 ,37], which is the applicati”