Comparing supervised and unsupervised category learning

By Bradley C. Love. 2002

246 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Developmental and Educational Psychology

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, neutral): “More introductory and detailed description of supervised and unsupervised methods can be found in review papers such as Kotsiantis et al. [86] and Love et al. [87].”
Category use and category learning. (2003, background): “…learn to classify the instances to some degree of accuracy, they are often given transfer classification in which 2 There is a growing interest in unsupervised learning in which people are not given feedback about the categories to which items belong (Clapper & Bower, 1994; Love, 2002, in press).”
SUSTAIN: a network model of category learning. (2004, background): “Focusing exclusively on a single learning mode is a serious limitation for any theory that intends to explain category learning and generalization in any comprehensive sense ( Love, 2001, 2002; Schank, Collins, & Hunter, 1986).”
A Rational Analysis of Rule-Based Concept Learning (2008, methodology): “Love et al., 2004) it is important for models of concept learning to account for human behavior over a range of learning situations and a variety of inference tasks. It should be possible to extend the Rational Rules model to unsupervised and semi-supervised learning tasks, by employing a “strong-sa”
Evaluating Amazon's Mechanical Turk as a Tool for Experimental Behavioral Research (2013, result): “A similar result was reported by Love (2002) [45] who found only a marginal Type II advantage compared to [38] to the results of Experiment 10.”
Design for a working memory. (2009, background): “Design for a Working Memory 89 Love (2002) provided evidence that higher dimensional categories, which are more difficult to learn than low-dimensional rule-based categories in a supervised learning regime, can be easier to learn in an unsupervised incidental learning regime.”