Bayesian Learning
13 A Comparative Study of Classification Algorithms: Statistical, Machine Learning and Neural Network R. D. King R. Henery
The aim of the Stat Log project is to compare the performance of statistical, machine learning, and neural network algorithms, on large real world problems. This paper describes the completed work on classification in the StatLog project. Classification is here defined to be the problem, given a set of multivariate data with assigned classes, of estimating the probability from a set of attributes describing a new example sampled from the same source that it has a pre-defined class. We gathered together a representative collection of algorithms from statistics (Naive Bayes, K-nearest Neighbour, Kernel density, Linear discriminant, Quadratic discriminant, Logistic regression, Projection pursuit, Bayesian networks), machine learning (CART, C4.5, NewID, AC2, CAL5, CN2, ITrule -- only propositional symbolic algorithms were considered), and neural networks (Backpropagation, Radial basis functions, Kohonen).
Submitted to MEDINF0.77
Almost one-half of the total cost of drugs spent in treating hospitalized patients is spend on antibiotics (1,2), and a significant part of this therapy is associated with serious misuse (2,3,4,5). One problem involves incorrect selection of a therapeutic regimen [4], while another involves the incorrect decision to administer any antibiotic (2,4,5). For example, one recent study concluded that one out of every four people in the United States was given penicillin during a recent year, and nearly 907. of these preL,criptions were oahccessary (6).