A Bayesian Decision Theoretical Approach to Supervised Learning, Selective Sampling, and Empirical Function Optimization

A Bayesian Decision Theoretical Approach to Supervised Learning, Selective Sampling, and Empirical Function Optimization

Many have used the principles of statistics and Bayesian decision theory to model specific learning problems. It is less common to see models of the processes of learning in general. One exception is the model of the supervised learning process known as the "Extended Bayesian Formalism" or...

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Bibliographic Details
Main Author: Carroll, James Lamond
Format: Others
Published: BYU ScholarsArchive 2010
Subjects:
machine learning
supervised learning
function optimization
empirical function optimization
statistics
Bayes
Bayes Law
Bayesian
Bayesian learning
decision theory
utility theory
unified Bayesian model
UBM
unified Bayesian decision theoretical model
UBDTM
learning framework
no-free-lunch
NFL
a priori learning
extended Bayesian formalism
EBF
bias
inductive bias
hypothesis space
function class
active learning
uncertainty sampling
query by uncertainty
query by committee
expected value of sample information
EVSI
Computer Sciences
Online Access:https://scholarsarchive.byu.edu/etd/2058
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3057&context=etd

Internet

https://scholarsarchive.byu.edu/etd/2058
https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3057&context=etd

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