摘要:

It has been claimed that connectionist methods of encoding compositional structures, as Pollack's recursive auto-associative memory (RAAM), support a non-classical form structure-sensitive operation known as 'holistic computation', where symbol structures be acted upon holistically without the need to decompose them, or to perform a search locate or access their constituents. In this paper, it is argued that the concept as described in the literature is vague and confused, and a revised definition of holistic computation proposed which aims to clarify the issues involved. It is also argued that holistic computation neither requires a highly distributed or holistic representation, nor is it unique to connectionist methods of representing compositional structure.

ISSN:0954-0091    

DOI:10.1080/095400998116558

出版商:Taylor & Francis Group    

年代:1998

数据来源: Taylor

摘要:

The hidden layer of backpropagation neural networks (NNs) holds the key to the networks' success in solving pattern classification problems. The units in the hidden layer encapsulate the network's internal representations of the outside world described by the input data. this paper, the hidden representations of trained networks are investigated by means simple greedy clustering algorithm. This clustering algorithm is applied to networks have been trained to solve well-known problems: the monks problems, the 5-bit problem and the contiguity problem. The results from applying the algorithm to problems with known concepts provide us with a better understanding of NN learning. These also explain why NNs achieve higher predictive accuracy than that of decision-tree methods. The results of this study can be readily applied to rule extraction from Production rules are extracted for the parity and the monks problems, as well as benchmark data set: Pima Indian diabetes diagnosis. The extracted rules from the Indian diabetes data set compare favorably with rules extracted from ARTMAP NNs terms of predictive accuracy and simplicity.

ISSN:0954-0091    

DOI:10.1080/095400998116567

出版商:Taylor & Francis Group    

年代:1998

数据来源: Taylor

摘要:

Departing from the customary view of the sigmoid thresholding function as a smooth transition non-linearity introduced into multi-layer perceptron (MLP) networks to a continuously differentiable albeit slow gradient descent toward an optimal solution minimizing some error norm, here a different, more fundamental viewpoint is proposed: the intrinsic dynamics throughout the network become those of the quadratic map of theory. This new viewpoint enables valuable insights into understanding the initial, intermediate and final dynamics of supervised learning of algorithms such as the widely used backpropagation scheme. More specifically, although approximately: the weight changes in the aforementioned three learning stages correspond to the three regimes fluctuation, periodicity and fixed points of the quadratic map. The purpose of this is to examine this basic idea, to support it theoretically, by example and through literature, and to suggest the next steps in its further investigation.

ISSN:0954-0091    

DOI:10.1080/095400998116576

出版商:Taylor & Francis Group    

年代:1998

数据来源: Taylor