Relations between prototype, exemplar, and decision bound models of categorization
Journal of Mathematical Psychology
The nature of statistical learning theory
The nature of statistical learning theory
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Computational models of inductive reasoning using a statistical analysis of a Japanese corpus
Cognitive Systems Research
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
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Mechanisms that underlie the inductive reasoning process in risk contexts are investigated. Experimental results indicate that people rate the same inductive reasoning argument differently according to the direction of risk aversion. In seeking to provide the most valid explanation of this, two kinds of models based on a Support Vector Machine (SVM) that process different knowledge spaces are proposed and compared. These knowledge spaces--a feature-based space and a category-based space--are both constructed from the soft clustering of the same corpus data. The simulation for the category-based model resulted in a slightly more successful replication of experimental findings for two kinds of risk conditions using two different estimated model parameters than the other simulation. Finally, the cognitive explanation by the category-based model based on a SVM for contextual inductive reasoning is discussed.