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Redistribution error is more in 50 and 500 numbers of learning cycles compared to 1000 and 5000 numbers of learning cycles.
Redistribution error is decreased when numbers of learning cycles are increased.
Performance of redistribution error using different numbers of learning cycles is shown in Fig. 7.
Fig. 6 Performance of AdaBoost.M2 model using different numbers of learning cycles.
It produces 80%% or more accuracy using 3000 or more numbers of learning cycles.
Accuracy of AdaBoost.M2 model is increased when numbers of learning cycles are increased from 50 to 5000.
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"We have identified a number of learning points from this incident, many of which have already been implemented as a result of the investigation and others which will be implemented over time.
The nursing union says that the number of learning disability nurses has been cut by a third – 1,700 – since 2010 and that training courses for specialists have fallen by 30% over the past decade.
With a number of learning methods available, a need for their comparison arises.
It shows that accuracy is increased in AdaBoost.M2 model when the number of learning cycles increases.
This means that Q-learning requires almost 38 times the number of learning episodes required for QA-learning.
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CEO of Professional Science Editing for Scientists @ prosciediting.com