Neural network learning and expert system. The MIT Press, NewYork, 1994.Gallant, S. (1994), Neural Network Learning and Expert Systems, MIT Press, Boston, MA.Neural Network Learning and Expert Systems, Cambridge, MA: MIT Press 2. Towell, G.G., Shavlik, J.W....
Conf. Learning Representations (ICLR, 2020). [30] DeVore, R., Hanin, B. & Petrova, G. Neural network approximation. Acta Numerica 30, 327–444 (2021). This work describes approximation properties of neural networks as they are presently understood and also discusses their performance with ot...
In this thesis we present "END", Expert Network Designer, an automated system for large structured computer networks design, modeling, simulation and performance evaluation.; END employs formalized network design experience to recommend the feasible network designs suitable for the particular user's netw...
PCANet: A Simple Deep Learning Baseline for Image Classification? In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded ... TH Chan,K Jia,S Gao,... - 《IEEE Transactions on Image ...
- 《Expert Systems with Application》 被引量: 0发表: 2010年 A hybrid decision tree-neural network approach for power system dynamic security assessment As we have seen, the application of automatic learning to security assessment may appear to be rather involved, and its use in real life will...
Prof. Siegelmann, Associate Professor at University of Massachusetts, neural computation, adaptive information systems, machine learning and knowledge discovery, theory of analog and adaptive systems, bioinformatics. She has published in a variety of prestigious journals, including Science, Theoretical Comput...
Reference18 proposed that the improved back propagation (BP) neural network optimized by heterogeneous comprehensive learning and the dynamic multi-swarm particle swarm optimizer (HPSO-BP) model and its prediction performance is superior to traditional models. Compared with the traditional linear ...
Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train ... A Khashman - 《Expert Systems with ...
The theory of artificial neural networks, which have already replaced humans in many problems, remains the most well-utilized branch of machine learning. Thus, one must select appropriate neural network architectures, data processing, and advanced applied mathematics tools. A common challenge for these...
4.2.1 Neural network A neural network is a computational learning system that uses a network of functions to understand and translate a data input of one form into the desired output, usually in another form [155]. Neural networks, in this context, refer to a set of neurons that could be...