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Hierarchical topological clustering learns stock market sectors
(Institute of Electrical and Electronics Engineers (IEEE), 2005)
The breakdown of financial markets into sectors provides an intuitive classification for groups of companies. The allocation of a company to a sector is an expert task, in which the company is classified by the activity ...
Optimising a Neural Tree Using Subtree Retraining
(2004)
Subtree retraining applied to a Stochastic Competitive Evolutionary Neural Tree model (SCENT) is introduced. This subtree retraining process is designed to improve the performance of the original model which provides a ...
Comparative performances of stochastic competitive evolutionary neural tree (SCENT) with neural classifiers
(2001)
A stochastic competitive evolutionary neural tree (SCENT) is described and evaluated against the best neural classifiers with equivalent functionality, using a collection of data sets chosen to provide a variety of clustering ...
The Analysis of the addition of Stochasticity to a Neural Tree Classifier
(2001)
This paper describes various mechanisms for adding stochasticity to a dynamic hierarchical neural clusterer. Such a network grows a tree-structured neural classifier dynamically in response to the unlabelled data with which ...
Optimising a neural tree classifier using a genetic algorithm
(2000)
This paper documents experiments performed using a GA to optimise the parameters of a dynamic neural tree model. Two fitness functions were created from two selected clustering measures, and a population of genotypes, ...