Instance-Based Learning Algorithms
Machine Learning
Study of weight importance in neural networks working with colineal variables in regression problems
IEA/AIE '99 Proceedings of the 12th international conference on Industrial and engineering applications of artificial intelligence and expert systems: multiple approaches to intelligent systems
A New Approach for Extracting Rules from a Trained Neural Network
EPIA '97 Proceedings of the 8th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
Neural Computing and Applications
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
Extracting rules from trained neural networks
IEEE Transactions on Neural Networks
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
Are artificial neural networks white boxes?
IEEE Transactions on Neural Networks
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Coniferous trees such as eucalyptus used to be preferred for papermaking because the cellulose fiber in the pulp of these species are longer, therefore making for stronger paper. In this study, the proposed neural network method solves in an efficient way, how to build prediction models in engineering. The system has been applied to predict amount of wood for production of paper, in which the coefficients can explain the variable with more influence over the variable to forecast. Obtaining a good prediction and as simple as possible, i.e. with the least number of forecast variables.