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
Risk context effects in inductive reasoning: an experimental and computational modeling study
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Computational models of inductive reasoning using a statistical analysis of a Japanese corpus
Cognitive Systems Research
| Hi-index | 0.01 |
A computational model of cognitive inductive reasoning that accounts for risk context effects is proposed. The model is based on a Support Vector Machine (SVM) that utilizes the kernel method. Kernel functions within the model are assumed to represent the functions of similarity computations based on distances between premise entities and conclusion entities in inductive reasoning arguments. Multipliers related to the kernel functions have the role of adjusting similarities and can explain rating shifts between two different risk contexts. Model fitting data supports the SVM-based model with kernel functions as a model of inductive reasoning in risk contexts. Finally, the paper discusses how the multipliers for kernel functions provide a satisfactory cognitive theoretical account of similarity adjustment.