نتایج جستجو برای: bayesian sopping rule

تعداد نتایج: 234752  

Journal: :Fuzzy Optimization and Decision Making 2022

Abstract In Bayesian rule an unknown parameter is thought to be a quantity whose variation can characterized by prior distribution. Then some data are observed from population distribution function indexed the and then updated according data. The named as posterior Based on uncertainty theory, this paper first makes connection between likelihood function, proposes new method obtain with given S...

The problems of sequential change-point have several important applications in quality control, signal processing, and failure detection in industry and finance and signal detection. We discuss a Bayesian approach in the context of statistical process control: at an unknown time  τ, the process behavior changes and the distribution of the data changes from p0 to p1. Two cases are consi...

2015
Abdallah Alashqur

Classification is an important data mining technique that is used by many applications. Several types of classifiers have been described in the research literature. Example classifiers are decision tree classifiers, rule-based classifiers, and neural networks classifiers. Another popular classification technique is naïve Bayesian classification. Naïve Bayesian classification is a probabilistic ...

2006
Clément Fauré Sylvie Delprat Jean-François Boulicaut Alain Mille

This paper concerns the iterative implementation of a knowledge model in a data mining context. Our approach relies on coupling a Bayesian network design with an association rule discovery technique. First, discovered association rule relevancy isenhanced by exploiting the expert knowledge encoded within a Bayesian network, i.e., avoiding to provide trivial rules w.r.t. known dependencies. More...

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

2007
Christoph F. Eick Yeong-Joon Kim Nicola Secomandi

The paper describes an inductive learning environment called DELVAUX for classiication tasks that learns PROSPECTOR-style, Bayesian classiication 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 oospring through the exchange of rules, permitting tter rule-sets to produce oospring ...

2007
Pedro Domingos

Although Bayesian model averaging (BMA) is in principle the optimal method for combining learned models, it has received relatively little attention in the machine learning literature. This article describes an extensive empirical study of the application of BMA to rule induction. BMA is applied to a variety of tasks and compared with more ad hoc alternatives like bagging. In each case, BMA typ...

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