Summary: IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART C: APPLICATIONS AND REVIEWS, VOL. 36, NO. 5, SEPTEMBER 2006 649
Classification Methods and Inductive Learning Rules:
What We May Learn From Theory
Cesare Alippi, Fellow, IEEE, and Pietro Braione
Abstract--Inductive learning methods allow the system designer
to infer a model of the relevant phenomena of an unknown process
by extracting information from experimental data. A wide range
of inductive learning methods is nowadays available, potentially
ensuring different levels of accuracy on different problem domains.
In this critical review of theoretic results gained in the last decade,
we address the problem of designing an inductive classification
system with optimal accuracy when domain knowledge is limited
and the number of available experiments is--possibly--small. By
analyzing the formal properties of consistent learning methods
and of accuracy estimators, we wish to convey to the reader the
message that the common practice of aggressively pursuing error
minimization with differentg training algorithms and classification
families is unjustified.
Index Terms--Image classification, intelligent systems, learning
systems, neural networks, pattern classification.