Naslov
Conditions for Occam's razor applicability and noise elimination

Autori
Gamberger, Dragan ; Lavrač, Nada

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Machine Learning: ECML-97 / Van Someren, Maarten ; Widmer, Gerhard - Berlin : Springer, 1997, 108-123

Skup
9th European Conference on Machine Learning

Mjesto i datum
Prag, Češka Republika, 23.04.1997. - 25.04.1997

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
machine learning; noise handling

Sažetak
The Occam's razor principle suggests that among all the correct hypotheses, the simplest hypothesis is the one which best captures the structure of the problem domain and has the highest prediction accuracy when classifying new instances. This principle is implicitly used also for dealing with noise, in order to avoid overfitting a noisy training set by rule truncation or by pruning of decision trees. This work gives a theoretical framework for the applicability of Occam's razor, developed into a procedure for eliminating noise from a training set. The results of empirical evaluation show the usefulness of the presented approach to noise elimination.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

POVEZANOST RADA