Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii
Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii
Masters Theses
“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …
Evolving Decision Trees For The Categorization Of Software, Jasenko Hosic
Evolving Decision Trees For The Categorization Of Software, Jasenko Hosic
Masters Theses
"Current manual techniques of static reverse engineering are inefficient at providing semantic program understanding. An automated method to categorize applications was developed in order to quickly determine pertinent characteristics. Prior work in this area has had some success, but a major strength of the approach detailed in this thesis is that it produces heuristics that can be reused for quick analysis of new data. The method relies on a genetic programming algorithm to evolve decision trees which can be used to categorize software. The terminals, or leaf nodes, within the trees each contain values based on selected features from one …
Semantic Preserving Text Tepresentation And Its Applications In Text Clustering, Michael Howard
Semantic Preserving Text Tepresentation And Its Applications In Text Clustering, Michael Howard
Masters Theses
Text mining using the vector space representation has proven to be an valuable tool for classification, prediction, information retrieval and extraction. The nature of text data presents several issues to these tasks, including large dimension and the existence of special polysemous and synonymous words. A variety of techniques have been devised to overcome these shortcomings, including feature selection and word sense disambiguation. Privacy preserving data mining is also an area of emerging interest. Existing techniques for privacy preserving data mining require the use of secure computation protocols, which often incur a greatly increased computational cost. In this paper, a generalization-based …