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Full-Text Articles in Physical Sciences and Mathematics
Analysis Of Music Genre Clustering Algorithms, Samuel Walter Stern
Analysis Of Music Genre Clustering Algorithms, Samuel Walter Stern
Theses and Dissertations
Classification and clustering of music genres has become an increasingly prevalent focusin recent years, prompting a push for research into relevant algorithms. The most successful algorithms have typically applied the Naive Bayes or k-Nearest Neighbors algorithms, or used Neural Networks to perform classification. This thesis seeks to investigate the use of unsupervised clustering algorithms such as K-Means or Hierarchical clustering, and establish their usefulness in comparison to or conjunction with established methods.
Correction Of Back Trajectories Utilizing Machine Learning, Britta F. Gjermo Morrison
Correction Of Back Trajectories Utilizing Machine Learning, Britta F. Gjermo Morrison
Theses and Dissertations
The goal of this work was to analyze 24-hour back trajectory performance from a global, low-resolution weather model compared to a high-resolution limited area weather model in particular meteorological regimes, or flow patterns using K-means clustering, an unsupervised machine learning technique. The duration of this study was from 2015-2019 for the contiguous United States (CONUS). Three different machine learning algorithms were tested to study the utility of these methods improving the performance of the CFS relative to the performance of the RAP. The aforementioned machine learning techniques are linear regression, Bayesian ridge regression, and random forest regression. These results mean …