A more rational model of categorization
A more rational model of categorization
复制标题
更合理的分类模型
DOI：
--
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
D. Navarro
中科院分区：
文献类型：
--
作者：
Adam N. Sanborn;T. Griffiths;D. Navarro
理性分类模型(RMC; Anderson；
The rational model of categorization (RMC; Anderson,
1990) assumes that categories are learned by cluster-
ing similar stimuli together using Bayesian inference.
As computing the posterior distribution over all assign-
ments of stimuli to clusters is intractable, an approxi-
mation algorithm is used. The original algorithm used
in the RMC was an incremental procedure that had no
guarantees for the quality of the resulting approxima-
tion. Drawing on connections between the RMC and
models used in nonparametric Bayesian density esti-
mation, we present two alternative approximation al-
gorithms that are asymptotically correct. Using these
algorithms allows the e®ects of the assumptions of the
RMC and the particular inference algorithm to be ex-
plored separately. We look at how the choice of inference
algorithm changes the predictions of the model.
影响因子：
4.1
作者：
NOSOFSKY, RM
通讯作者：
NOSOFSKY, RM