This project focuses on the field of meta-learning by investigating the relation between learning mechanisms and the tasks and domains where these mechanisms are applicable. An important activity of this project is to propose effective data structures and meta-features that characterize example distributions. We plan to extend model-based and information-theoretic characterizations, and to exploit multivariate density estimation techniques to generate a concise mapping of an example distribution. Results from this project will be used in the design and construction of meta-learning assistants that will be able to provide automatic and systematic user guidance for model selection and model ranking.
This project is multidisciplinary in nature, with a particular emphasis on physics and astronomy. The principal area of application lies in the classification of Mars landscapes from geo-morphological features.
A major goal of this project is to combine research and educational strategies aimed at the establishment of a Pattern Classification and Machine Learning Laboratory at the University of Houston. This laboratory will be used to analyze, learn, and perform predictions using real scientific data. It will also be used to help local youth, especially women and minorities, acquire a deep appreciation for science. Through the formation of science clubs and learning communities among high school and undergraduate students, this project strives to have a strong impact in fostering interest among young students in choosing scientific or technological paths as their professional career.
