摘要:

This paper addresses the connectionist accounts of two related topics, the representation of ‘type hierarchies’ and ‘cognitive categories’ (as the terms are used in knowledge representation research and cognitive psychology respectively). There are important problems with the current accounts of these topics, particularly the circularity of the definition of subvector type representations, and the inaccessibility to the network of prototype category representations. We introduce our own account of type and category, based on the identification of the ‘domain’ of units on which inputs of the same category are typically represented. Domains are formed by learning information about the co-activation of units in a population, and identified (for a given input by using this information to generate a ‘centrality’ value for each unit (collectively called a ‘centrality distribution’). The main claims of this paper are that centrality distributions can be used as representations of categories, and can be hierarchically organized to capture type information. We illustrate these claims with implementations, and discuss the representation of category gradedness, and mechanisms of property inheritance. The use of centrality distributions (as well (as patterns of activation)as representations is an extension to basic connectionist methods.

ISSN:0954-0091    

DOI:10.1080/09540098908915646

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

This paper introduces a new method called Dynamic Node Creation (DNC) which automatically grows BP networks until the target problem is solved. DNC sequentially adds nodes one at a time to the hidden layer(s) of the network until the desired approximation accuracy is achieved. Simulation results for parity, symmetry, binary addition, and the encoder problem are presented. The procedure was capable of finding known minimal topologies in many cases, and was always within three nodes of the minimum. Computational expense for finding the solutions was comparable to training normal BP networks with the same final topologies. Starting out with fewer nodes than needed to solve the problem actually seems to help find a solution. The method yielded a solution for every problem tried.

ISSN:0954-0091    

DOI:10.1080/09540098908915647

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

This article presents a case study in examining the bias of two particular formalisms: decision trees and linear threshold units. The immediate result is a new hybrid representation, called a ‘perceptron tree’, and an associated learning algorithm called the ‘percepton tree error correction procedure’. The longer term result is a model for exploring issues related to understanding representational bias and constructing other useful hybrid representations.

ISSN:0954-0091    

DOI:10.1080/09540098908915648

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

In this paper we studied the dynamic behavior of neural networks consisting of discrete populations of formal neurons. Neurons are assumed to have the same probability of connection with other neurons carrying the same type of chemical marker and divided in such a way to neural subpopulations due to the chemical affinity of markers carried by the individual cells. The dynamics of isolated networks as well as the ones that receive sustained external inputs show multiple stability and hysteresis phenomena which lead to multiple memory domains.

ISSN:0954-0091    

DOI:10.1080/09540098908915649

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

Most known learning algorithms for dynamic neural networks in non-stationary environments need global computations to perform credit assignment. These algorithms either are not local in time or not local in space. Those algorithms which are local in both time and space usually cannot deal sensibly with ‘hidden units’. In contrast, as far as we can judge, learning rules in biological systems with many ‘hidden units’ are local in both space and time. In this paper we propose a parallel on-line learning algorithms which performs local computations only, yet still is designed to deal with hidden units and with units whose past activations are ‘hidden in time’. The approach is inspired by Holland's idea of the bucket brigade for classifier systems, which is transformed to run on a neural network with fixed topology. The result is a feedforward or recurrent ‘neural’ dissipative system which is consuming ‘weight-substance’ and permanently trying to distribute this substance onto its connections in an appropriate way. Simple experiments demonstrating the feasibility of the algorithm are reported.

ISSN:0954-0091    

DOI:10.1080/09540098908915650

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

ISSN:0954-0091    

DOI:10.1080/09540098908915651

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

ISSN:0954-0091    

DOI:10.1080/09540098908915652

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

ISSN:0954-0091    

DOI:10.1080/09540098908915654

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor