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Data Mining With Newton's Method., James Dale Cloyd
Data Mining With Newton's Method., James Dale Cloyd
Electronic Theses and Dissertations
Capable and well-organized data mining algorithms are essential and fundamental to helpful, useful, and successful knowledge discovery in databases. We discuss several data mining algorithms including genetic algorithms (GAs). In addition, we propose a modified multivariate Newton's method (NM) approach to data mining of technical data. Several strategies are employed to stabilize Newton's method to pathological function behavior. NM is compared to GAs and to the simplex evolutionary operation algorithm (EVOP). We find that GAs, NM, and EVOP all perform efficiently for well-behaved global optimization functions with NM providing an exponential improvement in convergence rate. For local optimization problems, we …
Recurrent Neural Networks And Algorithms For Reconstruction Of Images From Noisy And/Or Partial Data, Ming-Jung Seow
Recurrent Neural Networks And Algorithms For Reconstruction Of Images From Noisy And/Or Partial Data, Ming-Jung Seow
Electrical & Computer Engineering Theses & Dissertations
In this thesis, modular architectures and neighborhood-distance based learning algorithms for fast and effective convergence with increased storage capacity of Hopfield neural networks are presented. The main objective of this research work is to better understand the function of recurrent neural networks and the influence of modularity within a network, and to design, implement, and test the performance of modular Hopfield neural networks for pattern association. Mathematical analysis and results are provided to show that the speed, storage capacity, and generalization capability of the recurrent networks are improved significantly by incorporating the modular architectures and learning algorithms. A new ratio …
The Effect Of Model Formulation On The Comparative Performance Of Artificial Neural Networks And Regression, Michael F. Cochrane
The Effect Of Model Formulation On The Comparative Performance Of Artificial Neural Networks And Regression, Michael F. Cochrane
Engineering Management & Systems Engineering Theses & Dissertations
Multiple linear regression techniques have been traditionally used to construct predictive statistical models, relating one or more independent variables (inputs) to a dependent variable (output). Artificial neural networks can also be constructed and trained to learn these complex relationships, and have been shown to perform at least as well as linear regression on the same data sets. Research on the use of neural network models as alternatives to multivariate linear regression has focused predominantly on the effects of sample size, noise, and input vector size on the comparative performance of these two modeling techniques. However, research has also shown that …