Article Instance

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{
    "pk": 32927,
    "title": "Concept Formation and Attention",
    "subtitle": null,
    "abstract": "In this paper, I combine the ideas of attention from cognitive psychology with concept formation in machine learning. M y claim is that the use of attention can lead to a more efficient learning system, without sacrificing accuracy. Attention leads to a savings in efficiency because it focuses only on the relevant attributes, retrieves less information from the environment, and is therefore less costly than a system that uses every piece of information available. I present a working dgorithm for attention, built onto the Classit concept formation system, and describe results from three domains.'",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Poster Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/6fj0c4xn",
    "frozenauthors": [
        {
            "first_name": "John",
            "middle_name": "H.",
            "last_name": "Gennari",
            "name_suffix": "",
            "institution": "Keio University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1991-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/32927/galley/23987/download/"
        }
    ]
}