We investigate the influence of different kinds of structure on the learning behaviour of a perceptron performing a classification task defined by a teacher rule. The underlying pattern distribution is permitted to have spatial correlations. The prior distribution for the teacher coupling vectors itself is assumed to be nonuniform. Thus classification tasks of quite different difficulty are inc...

This essay defends the view that inductive reasoning involves following inductive rules against objections that inductive rules are undesirable because they ignore background knowledge and unnecessary because Bayesianism is not an inductive rule. I propose that inductive rules be understood as sets of functions from data to hypotheses that are intended as solutions to inductive problems. Accord...

The certainty-factor (CF) model is a method for managing uncertainty in rule-based systems. Shortliffe and Buchanan (1975) developed the CF model in the mid-1970s for MYCIN, an expert system for the diagnosis and treatment of meningitis and infections of the blood. Since then, the CF model has become the standard approach to uncertainty management in rule-based systems. When the model was creat...

The main problem faced by all association rule/pattern mining algorithms is their production of a large number of rules which incurred a secondary mining problem; namely, mining interesting association rules/patterns. The problem is compounded by the fact that ‘common knowledge’ discovered rules are not interesting, but they are usually strong rules with high support and confidence levels – the...

We discuss a Bayesian discovery procedure for multiple comparison problems. We show that under a coherent decision theoretic framework, a loss function combining true positive and false positive counts leads to a decision rule based on a threshold of the posterior probability of the alternative. Under a semi-parametric model for the data, we show that the Bayes rule can be approximated by the o...

Medical outcomes are inexorably linked to patient illness and clinical interventions. Interventions change the course of disease, crucially determining outcome. Traditional outcome prediction models build a single classifier by augmenting interventions with disease information. Interventions, however, differentially affect prognosis, thus a single prediction rule may not suffice to capture vari...

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the exten...

Multivariate Bayesian models trained with machine learning, in conjunction with rule-based time-series statistical techniques, are explored for the purpose of improving patient monitoring. Three vital sign data streams and known outcomes for 36 intensive care unit (ICU) patients were captured retrospectively and used to train a set of Bayesian net models and to construct time-series models. Mod...

Decision making under uncertainty is commonly modelled as a process of competitive stochastic evidence accumulation to threshold (the drift-diffusion model). However, it is unknown how animals learn these decision thresholds. We examine threshold learning by constructing a reward function that averages over many trials to Wald’s cost function that defines decision optimality. These rewards are ...

Currently, there is renewed interest in the problem, raised by Shafer in 1985, of updating probabilities when observations are incomplete (or setvalued). This is a fundamental problem, and of articular interest for Bayesian networks. Recently, Griinwald and Halpern have shown that commonly used updating strategies fail here, except under very special assumptions. We propose a new rule for updat...