Matching Items (3)
Filtering by
- All Subjects: Machine Learning
- Creators: Cochran, Douglas
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Description
Divergence functions are both highly useful and fundamental to many areas in information theory and machine learning, but require either parametric approaches or prior knowledge of labels on the full data set. This paper presents a method to estimate the divergence between two data sets in the absence of fully labeled data. This semi-labeled case is common in many domains where labeling data by hand is expensive or time-consuming, or wherever large data sets are present. The theory derived in this paper is demonstrated on a simulated example, and then applied to a feature selection and classification problem from pathological speech analysis.
ContributorsGilton, Davis Leland (Author) / Berisha, Visar (Thesis director) / Cochran, Douglas (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Deforestation in the Amazon rainforest has the potential to have devastating effects on ecosystems on both a local and global scale, making it one of the most environmentally threatening phenomena occurring today. In order to minimize deforestation in the Amazon and its consequences, it is helpful to analyze its occurrence using machine learning architectures such as the U-Net. The U-Net is a type of Fully Convolutional Network that has shown significant capability in performing semantic segmentation. It is built upon a symmetric series of downsampling and upsampling layers that propagate feature information into higher spatial resolutions, allowing for the precise identification of features on the pixel scale. Such an architecture is well-suited for identifying features in satellite imagery. In this thesis, we construct and train a U-Net to identify deforested areas in satellite imagery of the Amazon through semantic segmentation.
ContributorsGiel, Joshua (Author) / Douglas, Liam (Co-author) / Espanol, Malena (Thesis director) / Cochran, Douglas (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Sustainability (Contributor)
Created2024-05
Description
Deforestation in the Amazon rainforest has the potential to have devastating effects
on ecosystems on both a local and global scale, making it one of the most environmentally
threatening phenomena occurring today. In order to minimize deforestation in the Ama-
zon and its consequences, it is helpful to analyze its occurrence using machine learning
architectures such as the U-Net. The U-Net is a type of Fully Convolutional Network that
has shown significant capability in performing semantic segmentation. It is built upon
a symmetric series of downsampling and upsampling layers that propagate feature infor-
mation into higher spatial resolutions, allowing for the precise identification of features
on the pixel scale. Such an architecture is well-suited for identifying features in satellite
imagery. In this thesis, we construct and train a U-Net to identify deforested areas in
satellite imagery of the Amazon through semantic segmentation.
ContributorsDouglas, Liam (Author) / Giel, Joshua (Co-author) / Espanol, Malena (Thesis director) / Cochran, Douglas (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05