Browsing by Author "McAllister, Richard Arthur"

Now showing 1 - 2 of 2

Extracting abstract spatio-temporal features of weather phenomena for autoencoder transfer learning
(Montana State University - Bozeman, College of Engineering, 2020) McAllister, Richard Arthur; Chairperson, Graduate Committee: John Sheppard
In this dissertation we develop ways to discover encodings within autoencoders that can be used to exchange information among neural network models. We begin by verifying that autoencoders can be used to make predictions in the meteorological domain, specifically for wind vector determination. We use unsupervised pre-training of stacked autoencoders to construct multilayer perceptrons to accomplish this task. We then discuss the role of our approach as an important step in positioning Empirical Weather Prediction as a viable alternative to Numerical Weather Prediction. We continue by exploring the spatial extensibility of the previously developed models, observing that different areas in the atmosphere may be influenced unique forces. We use stacked autoencoders to generalize across an area of the atmosphere, expanding the application of networks trained in one area to the surrounding areas. As a prelude to exploring transfer learning, we demonstrate that a stacked autoencoder is capable of capturing knowledge universal to these dataspaces. Following this we observe that in extremely large dataspaces, a single neural network covering that space may not be effective, and generating large numbers of deep neural networks is not feasible. Using functional data analysis and spatial statistics we analyze deep networks trained from stacked autoencoders in a spatiotemporal application area to determine the extent to which knowledge can be transferred to similar regions. Our results indicate high likelihood that spatial correlation can be exploited if it can be identified prior to training. We then observe that artificial neural networks, being essentially black-box processes, would benefit by having effective methods for preserving knowledge for successive generations of training. We develop an approach to preserving knowledge encoded in the hidden layers of several ANN's and collect this knowledge in networks that more effectively make predictions over subdivisions of the entire dataspace. We show that this method has an accuracy advantage over the single-network approach. We extend the previously developed methodology, adding a non-parametric method for determining transferrable encoded knowledge. We also analyze new datasets, focusing on the ability for models trained in this fashion to be transferred to operating on other storms.
FP-Tree motivated system for information retrieval using an abstraction path-based inverted index
(Montana State University - Bozeman, College of Engineering, 2009) McAllister, Richard Arthur; Chairperson, Graduate Committee: Rafal A. Angryk
Language ontologies provide an avenue for automated lexical analysis that may be used in concert with existing information retrieval methods to allow lexicographic relationships to be considered in searches. This paper presents a method of information retrieval that uses WordNet, a database of lexical ontologies, to generate paths of abstraction via lexicographic relationships, and uses these relationships as the basis for an inverted index to be used in the retrieval of documents from an indexed corpus. We present this method as a entree to a line of research in using lexical ontologies to perform graph analysis of documents, and through this process improve the precision of existing information retrieval techniques.