Naive (Bayes) at forty: The independence assumption in information retrieval

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• Machine learning in automated text categorization→Naive (Bayes) at forty: The independence assumption in information retrievalLegacy edge — source detail unavailable
• Thumbs up?→Naive (Bayes) at forty: The independence assumption in information retrievalLegacy edge — source detail unavailable
• Semi-Supervised Learning→Naive (Bayes) at forty: The independence assumption in information retrievalLegacy edge — source detail unavailable
• A comparison of event models for naive bayes text classification→Naive (Bayes) at forty: The independence assumption in information retrievalLegacy edge — source detail unavailable
• Text Classification from Labeled and Unlabeled Documents using EM→Naive (Bayes) at forty: The independence assumption in information retrievalLegacy edge — source detail unavailable
• Thumbs up? Sentiment Classification using Machine Learning Techniques→Naive (Bayes) at forty: The independence assumption in information retrievalLegacy edge — source detail unavailable
• Natural language processing: state of the art, current trends and challenges→Naive (Bayes) at forty: The independence assumption in information retrievalLegacy edge — source detail unavailable
• The text mining handbook: advanced approaches in analyzing unstructured data→Naive (Bayes) at forty: The independence assumption in information retrievalLegacy edge — source detail unavailable
• Naive (Bayes) at forty: The independence assumption in information retrieval→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• Naive (Bayes) at forty: The independence assumption in information retrieval→On the Optimality of the Simple Bayesian Classifier under Zero-One LossLegacy edge — source detail unavailable
• Naive (Bayes) at forty: The independence assumption in information retrieval→Information Retrieval: Data Structures and AlgorithmsLegacy edge — source detail unavailable
• Naive (Bayes) at forty: The independence assumption in information retrieval→The SMART Retrieval System—Experiments in Automatic Document ProcessingLegacy edge — source detail unavailable
• Naive (Bayes) at forty: The independence assumption in information retrieval→A sequential algorithm for training text classifiersLegacy edge — source detail unavailable
• Naive (Bayes) at forty: The independence assumption in information retrieval→Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybridLegacy edge — source detail unavailable
• Semi-Supervised Learning→A comparison of event models for naive bayes text classificationLegacy edge — source detail unavailable
• Semi-Supervised Learning→Text Classification from Labeled and Unlabeled Documents using EMLegacy edge — source detail unavailable
• Semi-Supervised Learning→On the Optimality of the Simple Bayesian Classifier under Zero-One LossLegacy edge — source detail unavailable
• Semi-Supervised Learning→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• A comparison of event models for naive bayes text classification→A sequential algorithm for training text classifiersLegacy edge — source detail unavailable
• A comparison of event models for naive bayes text classification→On the Optimality of the Simple Bayesian Classifier under Zero-One LossLegacy edge — source detail unavailable
• A comparison of event models for naive bayes text classification→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• The text mining handbook: advanced approaches in analyzing unstructured data→A sequential algorithm for training text classifiersLegacy edge — source detail unavailable
• The text mining handbook: advanced approaches in analyzing unstructured data→Text Classification from Labeled and Unlabeled Documents using EMLegacy edge — source detail unavailable
• The text mining handbook: advanced approaches in analyzing unstructured data→Machine learning in automated text categorizationLegacy edge — source detail unavailable
• The text mining handbook: advanced approaches in analyzing unstructured data→On the Optimality of the Simple Bayesian Classifier under Zero-One LossLegacy edge — source detail unavailable
• The text mining handbook: advanced approaches in analyzing unstructured data→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• The text mining handbook: advanced approaches in analyzing unstructured data→Thumbs up? Sentiment Classification using Machine Learning TechniquesLegacy edge — source detail unavailable
• Text Classification from Labeled and Unlabeled Documents using EM→A comparison of event models for naive bayes text classificationLegacy edge — source detail unavailable
• Text Classification from Labeled and Unlabeled Documents using EM→The SMART Retrieval System—Experiments in Automatic Document ProcessingLegacy edge — source detail unavailable
• Text Classification from Labeled and Unlabeled Documents using EM→A sequential algorithm for training text classifiersLegacy edge — source detail unavailable
• Text Classification from Labeled and Unlabeled Documents using EM→On the Optimality of the Simple Bayesian Classifier under Zero-One LossLegacy edge — source detail unavailable
• Text Classification from Labeled and Unlabeled Documents using EM→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• Machine learning in automated text categorization→A sequential algorithm for training text classifiersLegacy edge — source detail unavailable
• Machine learning in automated text categorization→Text Classification from Labeled and Unlabeled Documents using EMLegacy edge — source detail unavailable
• Machine learning in automated text categorization→On the Optimality of the Simple Bayesian Classifier under Zero-One LossLegacy edge — source detail unavailable
• Machine learning in automated text categorization→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• On the Optimality of the Simple Bayesian Classifier under Zero-One Loss→Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybridLegacy edge — source detail unavailable
• Thumbs up?→A comparison of event models for naive bayes text classificationLegacy edge — source detail unavailable
• Thumbs up?→On the Optimality of the Simple Bayesian Classifier under Zero-One LossLegacy edge — source detail unavailable
• Thumbs up?→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• Natural language processing: state of the art, current trends and challenges→A comparison of event models for naive bayes text classificationLegacy edge — source detail unavailable
• Thumbs up? Sentiment Classification using Machine Learning Techniques→A comparison of event models for naive bayes text classificationLegacy edge — source detail unavailable
• Thumbs up? Sentiment Classification using Machine Learning Techniques→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable