Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Deep Learning (2)
- Machine Learning (2)
- Bandwidth selection (1)
- Conditional variance function (1)
- Deep Embedding Kernel (1)
-
- Deep Kernel (1)
- Error Calculation (1)
- Kernel Methods (1)
- Kernel smoothing (1)
- Local MLE (1)
- Local composite quantile regression (1)
- Local polynomial smoothing (1)
- Loss Function (1)
- Nonparametric regression (1)
- Nonparametric volatility modeling (1)
- Ordinal Classification (1)
- Quantile regression (1)
- Supervised Learning (1)
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Ordinal Hyperplane Loss, Bob Vanderheyden
Ordinal Hyperplane Loss, Bob Vanderheyden
Doctor of Data Science and Analytics Dissertations
This research presents the development of a new framework for analyzing ordered class data, commonly called “ordinal class” data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop predictive classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize …
One And Two-Step Estimation Of Time Variant Parameters And Nonparametric Quantiles, Bogdan Gadidov
One And Two-Step Estimation Of Time Variant Parameters And Nonparametric Quantiles, Bogdan Gadidov
Doctor of Data Science and Analytics Dissertations
This dissertation develops and discusses several one-step and two-step smoothing methods of time variant nonparametric quantiles and time variant parameters from probability models. First, we investigate and develop nonparametric techniques for measuring extreme quantiles. The method involves aggregating data by an explanatory variable such as time and smoothing the resulting data with a nonparametric method like kernel, local polynomial or spline smoothing. We demonstrate both in application and simulation that this two-step procedure of quantile estimation is superior to the parametric quantile regression. We then develop a one-step method which combines the strength of maximum likelihood estimation with a local …
Deep Embedding Kernel, Linh Le
Deep Embedding Kernel, Linh Le
Doctor of Data Science and Analytics Dissertations
Kernel methods and deep learning are two major branches of machine learning that have achieved numerous successes in both analytics and artificial intelligence. While having their own unique characteristics, both branches work through mapping data to a feature space that is supposedly more favorable towards the given task. This dissertation addresses the strengths and weaknesses of each mapping method through combining them and forming a family of novel deep architectures that center around the Deep Embedding Kernel (DEK). In short, DEK is a realization of a kernel function through a newly deep architecture. The mapping in DEK is both implicit …