This paper presents PAC-learning analyses for instance-based learning algorithms for both sym- bolic and numeric-prediction ta.sks. The algo-.
This paper presents PAC-learning analyses for instance-based learning algorithms for both symbolic and numeric-prediction tasks. The algorithms analyzed ...
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In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances.
Feb 1, 2023 · Analyses of Instance-Based Learning Algorithms ; Authors. Marc K. Albert. David W. Aha ; Proceedings: Learning. Volume ; Issue: Proceedings of the ...
Instance-based Learning is a machine learning approach that makes predictions based on the similarity of new instances to previously seen instances.
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In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances.
Dec 11, 2021 · Abstract. This paper presents PAC-learning analyses for instance-based learning algorithms for both symbolic and numeric-prediction tasks.
This paper presents PAC-learning analyses for instance-based learning algorithms for both symbolic and numeric-prediction tasks, and shows that a bound on ...
Oct 4, 2021 · In this paper, a comparison between the statistical Q-learning algorithm and the cognitive IBL algorithm is presented. A well-known environment, ...
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a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training.
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