Eager learning

Eager learning

Eager learning (engl., „Eifriges Lernen“) ist eine Klasse von maschinellen Lernverfahren. Im Gegensatz zum lazy learning findet dabei die Modellbildung offline einmalig auf Basis der Trainingsdaten statt, und nicht online zur Zeit der Anfrage. Der Vorteil ist, dass dadurch zwar die Zeit des Trainierens durch die Modellbildung verlängert wird, aber die Abfragezeit deutlich verkürzt wird.[1]

Im Gegensatz zum lazy learning kann dabei allerdings die Modellbildung stets nur global über den kompletten Trainingsdatensatz erfolgen, nicht lokal um den Arbeitspunkt, da dieser zum Zeitpunkt des Trainierens/Lernens nicht bekannt ist.

Literatur

  • David W. Aha: Lazy learning. Kluwer Academic Publishers, Norwell 1997, ISBN 0-7923-4584-3.
  • Peter Auer: On Learning From Multi-Instance Examples. Empirical Evaluation of a Theoretical Approach. In: ICML '97: Proceedings of the Fourteenth International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco 1997, ISBN 1-55860-486-3, S. 21-29.

Einzelnachweise

  1. ↑ David W. Aha: Lazy learning. Kluwer Academic Publishers, Norwell 1997, ISBN 0-7923-4584-3.

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