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Whereas supervised concept induction is primarily used as a tool for optimizing the performance of problem solvers (starting with Samuel's checkers), the AF construct allows the transition of symbolic problems to signal-based problems.
We evaluate the performance of problem (1) by classification accuracy, and problem (2) by three multi-label learning evaluation metrics, i.e., Coverage, RankingLoss, and MacroF1 [ 37].
We evaluate the performance of problem (1) by classification accuracy Accuracy = # Unlabe l ed data c l assified correctly # Un l abe l ed data and problem (2) by two multi-label learning evaluation metrics Coverage and RankingLoss [ 28].
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The impact of the search space on the performance of problem-solving programs (problem solvers) has been known for long in Artificial Intelligence [7].
Finally, ML-k NN and BP-MLL were selected, as two recent high- performance representatives of problem adaptation methods.
The total number of the mathematics problems correctly finished served as the performance of mathematics problem test.
In this paper, we evaluate the performance of two problem decomposition methods for training feedforward and recurrent neural networks for chaotic time series problems.
In this approach experimental data is used to estimate values for the parameters of a statistical model of the performance of a problem solver.
The typical methodologies assessed the performance of the problem under the variability of the uncertain parameters by optimizing the expected value of the objective function.
Findings also revealed that there was positive and moderate significant correlation between overall meta-cognitive strategies and performance of Algebra problem solving (r = 0.394, p < 0.05).
Results indicated that presenting users with information corresponding to their cognitive task (and no more) improves the performance of their problem solving/alarm handling.
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