Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Artificial Intelligence and Robotics (4)
- Data Science (2)
- Databases and Information Systems (2)
- Applied Mathematics (1)
- Business (1)
-
- Business Intelligence (1)
- Electrical and Computer Engineering (1)
- Engineering (1)
- Management Information Systems (1)
- Management Sciences and Quantitative Methods (1)
- Other Applied Mathematics (1)
- Other Computer Sciences (1)
- Signal Processing (1)
- Software Engineering (1)
- Systems Architecture (1)
- Technology and Innovation (1)
- Institution
Articles 1 - 8 of 8
Full-Text Articles in Theory and Algorithms
Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu
Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu
Graduate Theses and Dissertations
With the development of artificial intelligence, automated decision-making systems are increasingly integrated into various applications, such as hiring, loans, education, recommendation systems, and more. These machine learning algorithms are expected to facilitate faster, more accurate, and impartial decision-making compared to human judgments. Nevertheless, these expectations are not always met in practice due to biased training data, leading to discriminatory outcomes. In contemporary society, countering discrimination has become a consensus among people, leading the EU and the US to enact laws and regulations that prohibit discrimination based on factors such as gender, age, race, and religion. Consequently, addressing algorithmic discrimination has …
Machine Learning With Topological Data Analysis, Ephraim Robert Love
Machine Learning With Topological Data Analysis, Ephraim Robert Love
Doctoral Dissertations
Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with …
Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos
Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos
Electronic Theses and Dissertations
Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification …
An Adaptive Weighted Average (Wav) Reprojection Algorithm For Image Denoising, Halimah Alsurayhi
An Adaptive Weighted Average (Wav) Reprojection Algorithm For Image Denoising, Halimah Alsurayhi
Electronic Thesis and Dissertation Repository
Patch-based denoising algorithms have an effective improvement in the image denoising domain. The Non-Local Means (NLM) algorithm is the most popular patch-based spatial domain denoising algorithm. Many variants of the NLM algorithm have proposed to improve its performance. Weighted Average (WAV) reprojection algorithm is one of the most effective improvements of the NLM denoising algorithm. Contrary to the NLM algorithm, all the pixels in the patch contribute into the averaging process in the WAV reprojection algorithm, which enhances the denoising performance. The key parameters in the WAV reprojection algorithm are kept fixed regardless of the image structure. In this thesis, …
Scalable Online Kernel Learning, Jing Lu
Scalable Online Kernel Learning, Jing Lu
Dissertations and Theses Collection (Open Access)
One critical deficiency of traditional online kernel learning methods is their increasing and unbounded number of support vectors (SV’s), making them inefficient and non-scalable for large-scale applications. Recent studies on budget online learning have attempted to overcome this shortcoming by bounding the number of SV’s. Despite being extensively studied, budget algorithms usually suffer from several drawbacks.
First of all, although existing algorithms attempt to bound the number of SV’s at each iteration, most of them fail to bound the number of SV’s for the final averaged classifier, which is commonly used for online-to-batch conversion. To solve this problem, we propose …
On The Role Of Genetic Algorithms In The Pattern Recognition Task Of Classification, Isaac Ben Sherman
On The Role Of Genetic Algorithms In The Pattern Recognition Task Of Classification, Isaac Ben Sherman
Masters Theses
In this dissertation we ask, formulate an apparatus for answering, and answer the following three questions: Where do Genetic Algorithms fit in the greater scheme of pattern recognition? Given primitive mechanics, can Genetic Algorithms match or exceed the performance of theoretically-based methods? Can we build a generic universal Genetic Algorithm for classification? To answer these questions, we develop a genetic algorithm which optimizes MATLAB classifiers and a variable length genetic algorithm which does classification based entirely on boolean logic. We test these algorithms on disparate datasets rooted in cellular biology, music theory, and medicine. We then get results from these …
An Introduction To The Theory And Applications Of Bayesian Networks, Anant Jaitha
An Introduction To The Theory And Applications Of Bayesian Networks, Anant Jaitha
CMC Senior Theses
Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating a graphical system to model the data. It then develops probability distributions over these variables. It explores variables in the problem space and examines the probability distributions related to those variables. It conducts statistical inference over those probability distributions to draw meaning from them. They are good means to explore a large set of data efficiently to make inferences. There are a number of real world applications that already exist and are being actively researched. This paper discusses the theory and applications of …
Feature Selection And Classification Methods For Decision Making: A Comparative Analysis, Osiris Villacampa
Feature Selection And Classification Methods For Decision Making: A Comparative Analysis, Osiris Villacampa
CCE Theses and Dissertations
The use of data mining methods in corporate decision making has been increasing in the past decades. Its popularity can be attributed to better utilizing data mining algorithms, increased performance in computers, and results which can be measured and applied for decision making. The effective use of data mining methods to analyze various types of data has shown great advantages in various application domains. While some data sets need little preparation to be mined, whereas others, in particular high-dimensional data sets, need to be preprocessed in order to be mined due to the complexity and inefficiency in mining high dimensional …