Eager learning

{{Short description|Type of machine learning method}}

In artificial intelligence, eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system.{{cite conference|url=https://books.google.com/books?id=GtcevX7n90wC&pg=PA158 |title=Hybrid algorithms with Instance-Based Classification|author=Hendrickx, Iris|author2=Van den Bosch, Antal|author-link2=Antal van den Bosch|date=October 2005|publisher=Springer|book-title=Machine Learning: ECML2005|pages=158–169|isbn=9783540292432 }}

The main advantage gained in employing an eager learning method, such as an artificial neural network, is that the target function will be approximated globally during training, thus requiring much less space than using a lazy learning system. Eager learning systems also deal much better with noise in the training data. Eager learning is an example of offline learning, in which post-training queries to the system have no effect on the system itself, and thus the same query to the system will always produce the same result.

The main disadvantage with eager learning is that it is generally unable to provide good local approximations in the target function.{{Cite book|title=INTRODUCTION TO KNOWLEDGE PROCESSING|pages=2}}

Eager LearningWouda, Frank J., et al. "Estimation of full-body poses using only five inertial sensors: an eager or lazy learning approach?." Sensors 16.12 (2016): 2138. | Lazy LearningAha, David W. "Lazy learning." Lazy learning. Dordrecht: Springer Netherlands, 1997. 7-10.

Model is created as soon as possible | Model creation is waited until the last point possible

Processing power consumed sooner | Processing power consumed later

Model is ready for query at once | Waits to create a model until the query