@techreport{oai:CiteSeerPSU:387590,
title = {Computational Machine Learning in Theory and Praxis},
address = {Surrey, UK},
annote = {The Pennsylvania State University CiteSeer Archives},
author = {Ming Li},
institution = {Royal Holloway and Bedford New College, University of
London},
month = {September},
number = {NC-TR-95-052},
type = {NeuroCOLT technical report series},
url = {http://www.neurocolt.com/abs/1995/../../tech_reps/1995/nc-tr-95-052.ps.gz},
year = {1995},
abstract = {In the last few decades a computational approach to
machine learning has emerged based on paradigms from
recursion theory and the theory of computation. Such
ideas include learning in the limit, learning by
enumeration, and probably approximately correct (pac)
learning. These models usually are not suitable in
practical situations. In contrast, statistics based
inference methods have enjoyed a long and distinguished
career. Currently, Bayesian reasoning in various forms,
minimum message length (MML) and minimum description
length (MDL), are widely applied approaches. They are
the tools to use with particular machine learning
praxis such as simulated annealing, genetic algorithms,
genetic programming, artificial neural networks, and
the like. These statistical inference methods select
the hypothesis which minimizes the sum of the length of
the description of the hypothesis (also called `model')
and the length of the description of the data relative
to the hypothesis. It app...},
citeseer-isreferencedby = {oai:CiteSeerPSU:52132;
oai:CiteSeerPSU:560308; oai:CiteSeerPSU:359110;
oai:CiteSeerPSU:561730; oai:CiteSeerPSU:530322}, rights = {unrestricted}, size = {20 pages}, oai = {oai:CiteSeerPSU:387590}, language = {en}, notes = {not a CP paper},
keywords = {ML }
}
::