Anzeige
Erschienen in:
01.08.2016
An Analytical Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias
Erschienen in: Cognitive Computation | Ausgabe 4/2016
EinloggenAktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Since extreme learning machine (ELM) was proposed, hundreds of studies have been conducted on this subject in various areas, from theoretical researches to practical applications. However, there are very few papers in the literature to reveal the reasons why in ELM classification the class with the highest output value is being chosen as the predicted class for a given input. In order to give a clear insight into this question, this paper analyzes the rationality of ELM reasoning from the perspective of its inductive bias. The analysis results show that the choice of highest output in ELM is reasonable for both binary and multiclass classification problems. In addition, to deal with multiclass problems ELM uses the well-known one-against-all strategy, in which unclassifiable regions may exist. This paper also gives a clear explanation on how ELM resolves the unclassifiable regions, through both analysis and experiments.