分类
人工智能
计算机科学
贝叶斯概率
班级(哲学)
概率逻辑
相似性(几何)
对象(语法)
启发式
机器学习
事件(粒子物理)
自然语言处理
数据挖掘
量子力学
图像(数学)
物理
作者
Daniel J. Navarro,Charles Kemp
出处
期刊:Psychological Review
[American Psychological Association]
日期:2017-07-13
卷期号:124 (5): 643-677
被引量:8
摘要
Every time we encounter a new object, action, or event, there is some chance that we will need to assign it to a novel category. We describe and evaluate a class of probabilistic models that detect when an object belongs to a category that has not previously been encountered. The models incorporate a prior distribution that is influenced by the distribution of previous objects among categories, and we present 2 experiments that demonstrate that people are also sensitive to this distributional information. Two additional experiments confirm that distributional information is combined with similarity when both sources of information are available. We compare our approach to previous models of unsupervised categorization and to several heuristic-based models, and find that a hierarchical Bayesian approach provides the best account of our data. (PsycINFO Database Record
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