Bayesian classifier

The Bayesian classifier works by building approximate probability distributions for a set of n features using examples of each class. To illustrate, if there are three classes, each described by 10 features (for the purposes of this discussion, a feature is just a real number) then the classifier will try to model three probability distributions in 10-dimensional space. These distributions can be thought of as spheres or clusters in feature space. The process... 

Information Systems Directorate, N. J. S. C., Houston, TX 77058. Fuzmac and the Bayesian classifier are NASA copyrighted and are used with permission from the Information Systems Directorate, NASA Johnson Space Center, Houston, TX 77058. 

Three algorithms have been implemented in both single and multiperspective environments. In this way any bias introduced by a single algorithm should be removed. The first is the statistical Naive Bayesian Classifier, ft reduces the decision-making problem to simple calculations of feature probabilities, ft is based on Bayes theorem and calculates the posterior probability of classes conditioned on the given unknown feature... 

N.A. Woody and S.D. Brown, Hybrid Bayesian networks making the hybrid Bayesian classifier robust to missing training data, J. Chemom, 17, 266-273 (2003). 

Naive Bayesian classifier Intestinal absorption (passive), blood-brain barrier penetration, serum protein binding Classifier No [17]... 

Woody, N.A. and Brown, S.D., Hybrid Bayesian Networks Making the Hybrid Bayesian Classifier Robust to Missing Training Data /. Chemom. 2003, 17, 266-273. 

Glick, M. et al. 2006. Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and Laplacian-modified naive Bayesian classifiers. J. Chem. Inf. Model. 46, 193-200. 

Bulashevska A, Eils R. 2006. Predicting protein sub-cellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains. BMC Bioinformatics... 

Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 1997 29 103-30. 

Bender, A., Mussa, H.Y., Glen, R.C. and Reihng, S. (2004b) Molecular similarity searching using atom environments, information-based feature selection, and a naive Bayesian classifier. /. Chem. Inf. Comput. Sci., 44, 170-178. 

The particular Bayesian classifier that we consider in this chapter is Bayesian linear discriminant analysis (BLDA). For BLDA one assumes that the class covariance matrices Sr are equal. A pooled covariance matrix is constructed as follows... 

Recursive partitioning, Bayesian classifier, logisi-tic regression, k-nearest neighbor, support vector machine... 

Becker, B., R. Kohavi, and D. Sommerfield. 1997. Visuahzing the simple Bayesian classifier. Presented at KDD Workshop on Issues in the Integration of Data Mining and Data Visualization, Newport Beach, Cahf., August 17, 1997. 

It should be pointed out that this approach can t strictly be used for TTS purposes as acoustic features (e.g. time in seconds) measured from the corpus waveforms were used in addition to features that would be available at run time. Following this initial work, a number of studies have used decision trees [264] [418], and a wide variety of other machine learning algorithms have been applied to the problem including memory based learning [77] [402], Bayesian classifiers [516], support vector machines [87] and neural networks [157]. Similar results are reported in most cases, and it seems that the most important factors in the success of a system are the features used and the quality and quantity of data rather than the particular machine learning algorithm used. 

For some of the oil types a trained classifier using only chromatograms of unweathered oils could classify chromatograms of the same oil after weathering with 100 % accuracy. Bayesian classifiers and the learning machine were used in these classifications. 

Konenko I. Semi-naive Bayesian classifier. EWSL-91 Proceedings of the European Working Session on Learning on Machine Learning. Heidelberg Springer 1991. p 206-219. 

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