overlearn

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o·ver·learn

 (ō′vər-lûrn′)
tr.v. o·ver·learned also o·ver·learnt (-lûrnt′), o·ver·learn·ing, o·ver·learns
To continue studying or practicing (something) after initial proficiency has been achieved so as to reinforce or ingrain the learned material or skill.
American Heritage® Dictionary of the English Language, Fifth Edition. Copyright © 2016 by Houghton Mifflin Harcourt Publishing Company. Published by Houghton Mifflin Harcourt Publishing Company. All rights reserved.

overlearn

(ˌəʊvəˈlɜːn)
vb (tr)
1. (Education) to study too intensely
2. (Psychology) to learn or practice repetitively, to the point of automaticity
Collins English Dictionary – Complete and Unabridged, 12th Edition 2014 © HarperCollins Publishers 1991, 1994, 1998, 2000, 2003, 2006, 2007, 2009, 2011, 2014
References in periodicals archive ?
While we must be cautious about "overlearning" the lessons of past conflicts--no future conflict will unfold the way that either Iraq or Afghanistan did, and future conflicts with near-peer adversaries will likely look nothing like these campaigns--Barry's effort here is a worthy one.
The well-known German psychologist Hermann Ebbinghaus (1913) proposed an overlearning approach to education, meaning for a person to master a certain body of knowledge, they must practice to the point of mastery.
In this study, this method is used to avoid overlearning on features caused by synergies.
By overlearning and by intellectualizing our quest for wisdom and spiritual knowledge, we forget to make room for the unknown and the mysterious.
This has been shown to be increasing the retention of information and impart the possibility of overlearning. [9] Both distributed practice and overlearning have been connected to higher examination marks and long-term retention of material.
The model also requires mastery and overlearning before a student is able to move on to the next level of instruction.
This relative overexposure leads to overlearning, which is conducive to (incidentally) retaining the stimuli, or material.
SVM has the characteristics of small sample learning and strong generalization ability, which can avoid the problems of overlearning and local minimum.
The higher C can result in overlearning state, which means training set classification accuracy is high while test set classification accuracy is too low.
However, the problems of ANN in overlearning, connection weight estimations, and the need of a large number of data pieces for system training make it difficult to apply for some short-term prediction.