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Topic: Binary classifier

	Naive Bayes classifier - Wikipedia, the free encyclopedia
		A naive Bayes classifier (also known as Idiot's Bayes) is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions.
		In practice, often k = 2 (binary classification) and r = 1 (Bernoulli variables as features) are common, and so the total number of parameters of the naive Bayes model is 2n + 1, where n is the number of binary features used for prediction.
		Like all probabilistic classifiers under the MAP decision rule, it arrives at the correct classification as long as the correct class is more probable than any other class; class probabilities do not have to be estimated very well.
	en.wikipedia.org /wiki/Naive_Bayesian_classification (1219 words)

	A Perceptron Classifier for Interactive Fiction Developed in Inform
		To demonstrate the perceptron classifier and as a test of its utility, it was trained and implemented to do a "real world" task that is done in an existing piece of interactive fiction.
		If the title is classified as masculine, a female character flirts with the player character and the player character is referred to as male.
		Although a large margin classifier would have been more suitable in terms of the information it provides, a simpler system, a perceptron, was trained to evaluate its performance.
	www.nickm.com /if/perform/perform_paper.html (2728 words)

	IAD Publications List
		Evaluation of Pattern Classifiers for Fingerprint and OCR Application, in Pattern Recognition, 27, pp.
		#313 Wilson, C. Effectiveness of Feature and Classifier Algorithms in Character Recognition Systems, NIST Interagency Report 4995, December 1992 and In D. D'Amato, editor, Volume 1906.
		NIST Interagency Report 5209 - Comparison of Handprinted Digit Classifiers,, June 1993.
	www.itl.nist.gov /iad/pubs/pubs2.html (9804 words)

	Publications 2005 (Site not responding. Last check: 2007-10-25)
		Bourgeat, P. and Meriaudeau, F. Classifier vote and Gabor filter banks for wafer segmentation.
		On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE; Agia Napa, Cyprus.
		Ng, A.; Greenfield, P., and Chen, S. A study of the impact of compression and binary encoding on SOAP performance.
	www.ict.csiro.au /page.php?did=141 (4713 words)

	NLP and Ontologies in Biomedicine (Site not responding. Last check: 2007-10-25)
		Identification of Patients with Congestive Heart Failure using a Binary Classifier: A Case Study.
		Extraction of protein interaction information from unstructured text using a context-free grammar.
		An Investigation of Various Information Sources for Classifying Biological names
	blimp.cs.queensu.ca /cate1_1.html (5912 words)

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