Relevance and insight in experimental studies

1 October 1996

journal article
Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Expert

Vol. 11 (5) , 11-12
https://doi.org/10.1109/64.539009

Abstract

Ains are essential because extensions to existing algorithms, althoughintuitively plausible, often make little difference in practice. Consider the naive Bayesianclassifier, a simple learning method that uses training data to estimate the conditional probabilitiesof attribute values given the class. Because naive Bayes assumes that each attribute is conditionallyindependent, given the class, it would seem easy to improve upon by using more sophisticatedmethods. However, both Kononenko...

Keywords

ARTIFICIAL INTELLIGENT
MACHINE LEARNING
RANDOM VARIABLES
EXPERIMENTAL DESIGN
RANDOM VARIABLE
MEDICAL DIAGNOSIS
TRAINING DATA
BAYESIAN METHODS
SYSTEM TESTING
LEARNING ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE
ROCKETS
SATISFIABILITY
NATURAL SCIENCE

This publication has 2 references indexed in Scilit:

Induction of recursive Bayesian classifiers
Published by Springer Nature ,1993
Semi-naive bayesian classifier
Published by Springer Nature ,1991