Title
A Combined Representation to Refine the Knowledge Using a Neuro-Symbolic Hybrid System applied in a Problem of Apple Classification
Abstract
In this paper we present the model of a Neuro- Symbolic Hybrid System (NSHS) that allows us to refine the knowledge associated to specific problem, for example, in problem of objects classification, where most of the systems of artificial vision use a numeric approach to solve the problem. In order to do this refinement we use one criterion of the NSHS known as, knowledge representation type. The knowledge representation type used in this paper is called combined representation, which is a combination among a local representation and a distributed representation. The proposed NSHS model allows the integration of the numeric and symbolic knowledge in order to obtain refinement knowledge. In this work, numeric knowledge comes from a vision system and symbolic knowledge comes from a human expert in apple classification. We give a brief description of each phase of the proposed model and analysis of the results obtained for every approach (symbolic, connectionist and hybrid) are made. The obtained results demonstrated that, if a lack of knowledge exists, the NSHS model can be used to refine the knowledge.
Year
DOI
Venue
2006
10.1109/CONIELECOMP.2006.5
CONIELECOMP
Keywords
Field
DocType
local representation,numeric approach,combined representation,neuro-symbolic hybrid system,numeric knowledge,symbolic knowledge,apple classification,nshs model,proposed nshs model,knowledge representation type,refinement knowledge,quality control,logic,vision system,knowledge representation,hybrid system,testing,expert systems,artificial neural networks,databases,machine vision
Knowledge representation and reasoning,Machine vision,Computer science,Expert system,Artificial intelligence,Artificial neural network,Distributed representation,Hybrid system,Machine learning,Connectionism,Artificial vision
Conference
ISBN
Citations 
PageRank 
0-7695-2505-9
2
0.39
References 
Authors
1
4