|
About
NeuroCOLT
Papers
Archive
Books
info@neurocolt.org
|
NeuroCOLT
Technical Report NC-TR-97-031
Bayes
Optimal Lazy Learning
Petri
Kontkanen, Petri Myllymaki, Tom Silander and Henry Tirri
University of Helsinki
Finland
Abstract
In this paper we present a new probabilistic formalization
of the lazy learning approach. In our Bayesian framework, moving from
the construction of an explicit hypothesis to a lazy learning approach,
where predictions are made by combining the training data at query
time, is equivalent to integrating out all the model parameters. Hence
in Bayesian Lazy Learning the predictions are made by using all the
(infinitely many) models. We present the formalization of this general
framework, and illustrate its use in practice in the case of the Naive
Bayes classifier model family. The Bayesian lazy learning approach
is validated empriically with public domain data sets and the results
are compared to the performance of the traditional, single model Naive
Bayes. The general framework described in this paper can be applied
with any formal model family, and to any discrete prediction task
where the number of simultaneously predicted attributes is small,
which includes for example all classification tasks prevalent in the
machine learning literature.
Download Compressed
Postscript
Title
Page
|