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What is Bayesian Learning

Chapter 25
Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
A learning technique that determines model parameters (such as the network weights) by maximizing the posterior probability of the parameters given the training data. The idea is that some parameter values are more consistent with the observed data than others. By Bayes’ rule, maximizing the posterior probability amounts to maximizing the so-called model evidence, defined as the conditional probability of the training data given the model parameters. The evidence often can be approximated by some closed formula or by an update rule. Bayesian techniques render it possible to use all data for training instead of reserving patterns for cross-validation of parameters.
Published in Chapter:
Counting the Hidden Defects in Software Documents
Frank Padberg (Saarland University, Germany)
Copyright: © 2010 |Pages: 20
DOI: 10.4018/978-1-60566-766-9.ch025
Abstract
The author uses neural networks to estimate how many defects are hidden in a software document. Input for the models are metrics that get collected when effecting a standard quality assurance technique on the document, a software inspection. For inspections, the empirical data sets typically are small. The author identifies two key ingredients for a successful application of neural networks to small data sets: Adapting the size, complexity, and input dimension of the networks to the amount of information available for training; and using Bayesian techniques instead of cross-validation for determining model parameters and selecting the final model. For inspections, the machine learning approach is highly successful and outperforms the previously existing defect estimation methods in software engineering by a factor of 4 in accuracy on the standard benchmark. The author’s approach is well applicable in other contexts that are subject to small training data sets.
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Chapter 55
Data Mining Applications in a Medical System: A Case Study
Bayesian learning methods is able to provide useful practical solutions and forecasting features toward solving complicated problems.
Published in Chapter: Data Mining Applications in a Medical System: A Case Study; From: Encyclopedia of Business Analytics and Optimization
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Chapter 15
Prediction of Different Diseases in Upper Abdominal Organs Using Machine Learning Approach
It is a machine learning based on observed data in which learner tries to find out most probable hypothesis ‘h’ from a set of hypothesis ‘H’.
Published in Chapter: Prediction of Different Diseases in Upper Abdominal Organs Using Machine Learning Approach; From: Bio-Inspired Intelligence for Smart Decision-Making
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