Inferring the hypothesis spaces underlying inductive generalization. 2014. Tauber, Sean;; Navarro, Daniel;; Prefors, Amy;; Lee, Michael ... Main Content Metrics
We develop an approach in which a hypothesis space can be inferred from human generalization data. By defining the likelihood function relating human ...
We develop an approach in which a hypothesis space can be inferred from human generalization data. By defining the likelihood function relating human ...
We develop an approach in which a hypothesis space can be inferred from human generalization data. By defining the likelihood function relating human ...
Inductive inference is based on a generalization from a finite set of past observations, extending the observed pattern or relation to other future instances.
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Sep 25, 2024 · Historically, the only theoretical guarantee has been that if the hypothesis class is countable, inductive inference is possible, as exemplified ...
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A plausible alternative hypothesis is that inferences will be guided by base–target similarity alone. This alternative predicts no differences between sampling ...
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In this sense, inferring the hypotheses most likely to have generated the observed data guides the learner in generalizing beyond the data to new situations.
We argue that human inductive generalization is best explained in a Bayesian framework, rather than by traditional models based on similarity computations.
Induction is inferring general rules and theories from specific empirical data. Induction can be viewed as inverse deduction. Find a hypothesis h from data ...