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Teaching machines for learning machines.
Since training is a critical infrastructure for recognition engines, the research on training is significant for learning machines.
We have experimentally tested the memory algorithms on the MNIST database of handwritten digits, a common benchmark for learning machines.
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In the present study, we propose a method for using learning machines efficiently in an evolving context.
This risk function is the expected value of the test (or validation) error for a trained learning machine.
We believe there are several advantages to the learning machine approach for risk effect estimation.
In this study, SVM (Support Vector Machine) was applied for machine learning approach [ 58].
Rather, data sets of limited size (comprising, for example, active and inactive compounds) are often sufficient for machine learning.
He considered autocorrect on touchscreen keyboards a modern victory for machine learning.
● Atlas ML is a development platform for machine learning.
Neural networks are one type of model for machine learning.
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