By Topic

Pattern Recognition as Rule-Guided Inductive Inference

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Ryszard S. Michalski ; Department of Computer Science, University of Illinois, Urbana, IL 61801.

The determination of pattern recognition rules is viewed as a problem of inductive inference, guided by generalization rules, which control the generalization process, and problem knowledge rules, which represent the underlying semantics relevant to the recognition problem under consideration. The paper formulates the theoretical framework and a method for inferring general and optimal (according to certain criteria) descriptions of object classes from examples of classification or partial descriptions. The language for expressing the class descriptions and the guidance rules is an extension of the first-order predicate calculus, called variable-valued logic calculus VL21. VL21 involves typed variables and contains several new operators especially suited for conducting inductive inference, such as selector, internal disjunction, internal conjunction, exception, and generalization. Important aspects of the theory include: 1) a formulation of several kinds of generalization rules; 2) an ability to uniformly and adequately handle descriptors (i.e., variables, functions, and predicates) of different type (nominal, linear, and structured) and of different arity (i.e., different number of arguments); 3) an ability to generate new descriptors, which are derived from the initial descriptors through a rule-based system (i.e., an ability to conduct the so called constructive induction); 4) an ability to use the semantics underlying the problem under consideration. An experimental computer implementation of the method is briefly described and illustrated by an example.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-2 ,  Issue: 4 )