Utilizing Connectionist Learning Procedures in Symbolic Case Retrieval Nets
نویسنده
چکیده
This paper describes a method which, under certain circumstances, allows to automatically learn or adjust similarity measures. For this, ideas of connectionist learning procedures, in particular those related to Hebbian learning, are combined with a Case-Based Reasoning engine.
منابع مشابه
Journal of Applied Logic Special Volume on Neural-Symbolic Systems
The past 25 years have witnessed a tremendous progress in the nature and scope of Logic and its application in Computing Science. At the same time, distributed learning systems and, in particular, Neural Networks, have been playing a central role in the development of Artificial Intelligence (AI). Notwithstanding, human beings do not use these techniques in isolation, but in an integrated way. ...
متن کاملSymbolic vs. Connectionist Learning: An Experimental Comparison in a Structured Domain
ÐDuring the last two decades, the attempts of finding effective and efficient solutions to the problem of learning any kind of structured information have been splitting the scientific community. A aholy waro has been fought between the advocates of a symbolic approach to learning and the advocates of a connectionist approach. One of the most repeated claims of the symbolic party has been that ...
متن کاملNeural-symbolic integration
The field of neural-symbolic integration has received much attention recently. While with propositional paradigms, the integration of symbolic knowledge and connectionist systems (also called artificial neural networks) has already resulted in applicable systems, the theoretical foundations for the first-order case are currently being laid and first perspectives for real implementations are eme...
متن کاملCharacteristics of Data Suitable for Learning with Connectionist and Symbolic Methods
We contribute to a taxonomy of data with respect to appropriate selection of machine learning methods. Artificial data sets are carefully designed to expose the biases in a symbolic learning method and two connectionist learning methods. The data sets are based on the p-type and s-type classifications used in the past. Earlier results with these data sets are confirmed. New results indicate tha...
متن کاملA general framework for adaptive processing of data structures
A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to relatively poor structures, like arrays or sequences. The framework described in this paper is an attempt to unify adaptive models like artificial neural nets and belief nets for the problem of processing structured infor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007