Article Instance

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{
    "pk": 32971,
    "title": "A Continum of Induction Methods for Learning Probability Distributions with Generalization",
    "subtitle": null,
    "abstract": "Probabilistic models of pattern completion have several advantages, namely, ability to handle arbitrary conceptual representations including compositional structures, and explicitness of distributional assumptions. However, a gap in the theory of induction of priors has hindered probabilistic modeling of cognitive generalization bitises. W e propose a family of methods parameterized along a value 7 that controls the degree to which the probability distribution being induced generalizes from the training set. The extremes of the 7-continuum correspond to relative frequency methods and extreme maximum entropy methods. The methods apply to a wide range of pattern representations including simple feature vectors as well as frame-like feature DAGs.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Poster Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/6cc7r26d",
    "frozenauthors": [
        {
            "first_name": "Dekai",
            "middle_name": "",
            "last_name": "Wu",
            "name_suffix": "",
            "institution": "University of California at Berkeley",
            "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/32971/galley/24032/download/"
        }
    ]
}