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Geometric Implications of the Naive Bayes Assumption
, 1996
"... A Naive (or Idiot) Bayes network is a network with a single hypothesis node and several observations that are conditionally independent given the hypothesis. We recently surveyed a number of members of the UAI community and discovered a general lack of understanding of the implications of the Naive ..."
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Cited by 9 (0 self)
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Bayes assumption on the kinds of problems that can be solved by these networks. It has long been recognized [Minsky 61] that if observations are binary, the decision surfaces in these networks are hyperplanes. We extend this result (hyperplane separability) to Naive Bayes networks with m
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
, 1998
"... The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assump tions made abou ..."
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Cited by 499 (1 self)
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The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assump tions made
A comparison of event models for Naive Bayes text classification
, 1998
"... Recent work in text classification has used two different firstorder probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multivariate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey ..."
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Cited by 1025 (26 self)
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Recent work in text classification has used two different firstorder probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multivariate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e
On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes
, 2001
"... We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is i ..."
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Cited by 520 (8 self)
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We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size
Bayesian Network Classifiers
, 1997
"... Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less restr ..."
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Cited by 796 (20 self)
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Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less
Text Classification from Labeled and Unlabeled Documents using EM
 MACHINE LEARNING
, 1999
"... This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large qua ..."
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Cited by 1033 (15 self)
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quantities of unlabeled documents are readily available. We introduce an algorithm for learning from labeled and unlabeled documents based on the combination of ExpectationMaximization (EM) and a naive Bayes classifier. The algorithm first trains a classifier using the available labeled documents
Supervised and unsupervised discretization of continuous features
 in A. Prieditis & S. Russell, eds, Machine Learning: Proceedings of the Twelfth International Conference
, 1995
"... Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify de ning characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised dis ..."
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Cited by 540 (11 self)
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discretization method, to entropybased and puritybased methods, which are supervised algorithms. We found that the performance of the NaiveBayes algorithm signi cantly improved when features were discretized using an entropybased method. In fact, over the 16 tested datasets, the discretized version of NaiveBayes
Visual categorization with bags of keypoints
 In Workshop on Statistical Learning in Computer Vision, ECCV
, 2004
"... Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of im ..."
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Cited by 1005 (14 self)
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of image patches. We propose and compare two alternative implementations using different classifiers: Naïve Bayes and SVM. The main advantages of the method are that it is simple, computationally efficient and intrinsically invariant. We present results for simultaneously classifying seven semantic visual
Thumbs up? Sentiment Classification using Machine Learning Techniques
 IN PROCEEDINGS OF EMNLP
, 2002
"... We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform humanproduced baselines. However, the three mac ..."
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Cited by 1101 (7 self)
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machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topicbased categorization. We conclude by examining factors that make the sentiment classification problem more challenging
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