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Traill (2008, espec.Table "S" on p.31) follows Jerne and Popper in seeing this strategy as probably underlying all knowledge-gathering systems — at least in their initial phase.
In reinforcement learning, error-driven learning is a method for adjusting a model's (intelligent agent's) parameters based on the difference between its output results and the ground truth. These models stand out as they depend on environmental feedback, rather than explicit labels or categories. [ 1 ]
Learning Engineering is the systematic application of evidence-based principles and methods from educational technology and the learning sciences to create engaging and effective learning experiences, support the difficulties and challenges of learners as they learn, and come to better understand learners and learning. It emphasizes the use of ...
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When bootstrap aggregating is performed, two independent sets are created. One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement.
The errorless learning procedure is highly effective in reducing the number of responses to the S− during training. In Terrace's (1963) experiment, subjects trained with the conventional discrimination procedure averaged over 3000 S− (errors) responses during 28 sessions of training; whereas subjects trained with the errorless procedure averaged only 25 S− (errors) responses in the same ...
In computing, a roundoff error, [1] also called rounding error, [2] is the difference between the result produced by a given algorithm using exact arithmetic and the result produced by the same algorithm using finite-precision, rounded arithmetic. [3]
For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is −0.05 meters.