Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval

By David D. Lewis. 1998

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Citations in the corpus

Interval Coded Scoring: a toolbox for interpretable scoring systems (2018, methodology): “Well-known approaches include Support Vector Machines (SVM) (Vapnik, 1995), Naive Bayes (NB) classiﬁers (Lewis, 1998), decision trees (DT) (Quinlan, 1986), random forests (RF) (Ho, 1995) and neural networks (NN) (Bishop, 1995).”
One-shot-learning gesture recognition using HOG-HOF features (2013, methodology): “An evolution of Bag-of-Words (Lewis, 1998), a method used in document analysis, where each document is represented using the apparition frequency of each word in a dictionary, is one of the most popular in Computer Vision.”
Thumbs up? Sentiment Classification using Machine Learning Techniques (2002, background): “Then, each document d is represented by the document vector ~ d := ( n 1 ( d ) , n 2 ( d ) , . . . , n m ( d )).”
Evidence on the Information Content of Text in Analyst Reports (2014, methodology): “The naïve Bayes algorithm is one of the most successful classification techniques in the information retrieval and computational linguistics literature (Lewis 1998).”
Identification of Patients with Congestive Heart Failure using a Binary Classifier: A Case Study. (2003, background): “Linear Least Squares Fit [2], Support Vector Machines, Decision trees, Bayesean learning [3], symbolic rule induction [4], maximum entropy [5], expert networks [6] are just a few that have been applied to classifying e-mail, Web pages, newswire articles, medical reports among other documents.”
A Study of Approaches to Hypertext Categorization (2002, unknown): “Naive Bayes is a simple but effective text classification algorithm (Lewis, 1998; McCallum and Nigam, 1998).”
Some Effective Techniques for Naive Bayes Text Classification (2006, background): “Bayes have been studied extensively by some researchers [2], [ 3 ], [4].”
TEXT CLASSIFICATION AND CLASSIFIERS: A SURVEY (2012, methodology): “There are two event model are present for naive Bias [16] [17] [18] as multivariate Bernoulli and multinomial model.”
Neighbor-weighted K-nearest neighbor for unbalanced text corpus (2005, methodology): “A number of machine learning algorithms have been introduced to deal with text classiﬁcation, such as K-Nearest Neighbor (KNN) (Yang, 1999; Yang & Lin, 1999), Centroid-Based Classiﬁer (Han & Karypis, 2000), Naive Bayes (Lewis, 1998), Decision Trees (Lewis & Ringuette, 1994) and Support Vector Machin”
Violence Detection in Video Using Computer Vision Techniques (2011, methodology): “Approaches based on feature descriptors typically use the well-known bagof-words framework [14, 7].”
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017, background): “The original work can be found in [88].”