Abstract category learning
نویسندگان
چکیده
Category Learning Atsushi Hashimoto and Haruo Hosoya Department of Computer Science The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan Abstract. Motivated by a neurophysiological experiment on prefrontal cortex, we study a scheme for learning abstract categories. An abstract category represents a set of vectors that are identical to each other modulo substitution, e.g., ’ABAB’, ’BABA’, ’ACAC’, etc. We employ a clusteringbased unsupervised learning method for such abstract categories, in which the recognition step is reduced to the problem of maximal perfect weight matching. Our simulations using artificial inputs confirm that the scheme learns abstract categories robustly even with a certain level of noise in the inputs. Motivated by a neurophysiological experiment on prefrontal cortex, we study a scheme for learning abstract categories. An abstract category represents a set of vectors that are identical to each other modulo substitution, e.g., ’ABAB’, ’BABA’, ’ACAC’, etc. We employ a clusteringbased unsupervised learning method for such abstract categories, in which the recognition step is reduced to the problem of maximal perfect weight matching. Our simulations using artificial inputs confirm that the scheme learns abstract categories robustly even with a certain level of noise in the inputs.
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