You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
20 August 1992Inductive learning using generalized distance measures
This paper briefly reviews the two currently dominant paradigms in machine learning--the connectionist network (CN) models and symbol processing (SP) systems; argues for the centrality of knowledge representation frameworks in learning; examines a range of representations in increasing order of complexity and measures of similarity or distance that are appropriate for each of them; introduces the notion of a generalized distance measure (GDM) and presents a class of GDM-based inductive learning algorithms (GDML). GDML are motivated by the need for an integration of symbol processing (SP) and connectionist network (CN) approaches to machine learning. GDM offer a natural generalization of the notion of distance or measure of mismatch used in a variety of pattern recognition techniques (e.g., k-nearest neighbor classifiers, neural networks using radial basis functions, and so on) to a range of structured representations such strings, trees, pyramids, association nets, conceptual graphs, etc. which include those used in computer vision and syntactic approaches to pattern recognition. GDML are a natural extension of generative or constructive learning algorithms for neural networks that enable an adaptive and parsimonious determination of the network topology as well as the desired weights as a function of learning Applications of GDML include tasks such as planning, concept learning, and 2- and 3-dimensional object recognition. GDML offer a basis for a natural integration of SP and CN approaches to the construction of intelligent systems that perceive, learn, and act.
The alert did not successfully save. Please try again later.
Vasant Honavar, "Inductive learning using generalized distance measures," Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); https://doi.org/10.1117/12.139960