The paper describes an inductive learning environment called DELVAUX for classiication tasks that learns PROSPECTOR-style, Bayesian rules from sets of examples. A genetic algorithm approach is used for learning Bayesian rule-sets, in which a population consists of sets of rule-sets that generate oosprings through the exchange of rules, permitting tter rule-sets to produce oosprings with a highe...

The naive Bayesian classiier provides a simple and eeective approach to classiier learning, but its attribute independence assumption is often violated in the real world. A number of approaches have sought to alleviate this problem. A Bayesian tree learning algorithm builds a decision tree, and generates a local naive Bayesian classiier at each leaf. The tests leading to a leaf can alleviate at...

A medical diagnosis system (DRCAD), which consists of two sub-modules Bayesian and rule-based inference models, is presented in this study. Three types of tests are conducted to assess the performances of the models producing synthetic data based on the ALARM network. The results indicate that the linear combination of the aforementioned models leads to a 5% and a 30% improvement in medical dia...

In this paper we examine the use of Bayesian networks (BNs) for improving weather prediction, applying them to the problem of predicting sea breezes. We compare a pre-existing Bureau of Meteorology rule-based system with an elicited BN and others learned by two data mining programs, TETRAD II Spirtes et al., 1993] and Causal MML Wallace and Korb, 1999]. These Bayesian nets are shown to signiica...

We propose a modular neural-network structure for implementing the Bayesian framework for learning and inference. Our design has three main components, two for computing the priors and likelihoods based on observations and one for applying Bayes’ rule. Through comprehensive simulations we show that our proposed model succeeds in implementing Bayesian learning and inference. We also provide a no...

A recently proposed Bayesian modeling framework for classification facilitates both the analysis and optimization of error estimation performance. The Bayesian error estimator is then defined to have optimal mean-square error performance, but in many situations closed-form representations are unavailable and approximations may not be feasible. To address this, we present a method to optimally c...

Recently, new approaches to adaptive control have sought to reformulate the problem as a minimization of a relative entropy criterion to obtain tractable solutions. In particular, it has been shown that minimizing the expected deviation from the causal input-output dependencies of the true plant leads to a new promising stochastic control rule called the Bayesian control rule. This work proves ...

In this paper, we presented an optimal iterative decision rule for minimizing total cost in designing a sampling plan for machine replacement problem using the approach of dynamic programming and Bayesian inferences. Cost of replacing the machine and cost of defectives produced by machine has been considered in model. Concept of control threshold policy has been applied for decision making. If ...

Clinical trial designs often incorporate a sequential stopping rule to serve as a guide in the early termination of a study. When choosing a particular stopping rule, it is most common to examine frequentist operating characteristics such as type I error, statistical power, and precision of confidence intervals (Statist. Med. 2005, in revision). Increasingly, however, clinical trials are design...

Chapter 1: INTRODUCTION Chapter 2: SHAPE-BASED DESCRIPTION 2.1 Fourier Descriptors 2.2 Moment Invariants 2.3 Scalar Descriptors Chapter 3: CLASSIFICATION 3.1 Supervised v Unsupervised Classification 3.2 Classification using Bayes Theorem 3.3 Implementing Bayesian Classification Chapter 4: COMBINING CLASSIFIERS 4.1 The Fusion Model 4.2 Theory 4.2.1 The Product Rule 4.2.2 Sum Rule 4.3 Classifier ...