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Full-Text Articles in Statistical Models
Data-Adaptive Kernel Support Vector Machine, Xin Liu
Data-Adaptive Kernel Support Vector Machine, Xin Liu
Electronic Thesis and Dissertation Repository
In this thesis, we propose the data-adaptive kernel Support Vector Machine (SVM), a new method with a data-driven scaling kernel function based on real data sets. This two-stage approach of kernel function scaling can enhance the accuracy of a support vector machine, especially when the data are imbalanced. Followed by the standard SVM procedure in the first stage, the proposed method locally adapts the kernel function to data locations based on the skewness of the class outcomes. In the second stage, the decision rule is constructed with the data-adaptive kernel function and is used as the classifier. This process enlarges …
Visualizing Lab And Phenotype Associations Using Phewas And Electronic Health Records, Brenda Emerson, Miriam Goldman, Sahiti Kolli
Visualizing Lab And Phenotype Associations Using Phewas And Electronic Health Records, Brenda Emerson, Miriam Goldman, Sahiti Kolli
Honors Projects
As the digitization of patient health records is becoming more common, we are given a great opportunity to analyze these records and hopefully make discoveries about diseases or medicines. Being given large datasets of Electronic Health Records, I and two other students decided to look for novel phenotype associations with mean lab values, look to see whether the presence of a lab had associations with a phenotype, and create an interactive application to visual the associations between labs and phenotypes.
Quantifying The Effect Of The Shift In Major League Baseball, Christopher John Hawke Jr.
Quantifying The Effect Of The Shift In Major League Baseball, Christopher John Hawke Jr.
Senior Projects Spring 2017
Baseball is a very strategic and abstract game, but the baseball world is strangely obsessed with statistics. Modern mainstream statisticians often study offensive data, such as batting average or on-base percentage, in order to evaluate player performance. However, this project observes the game from the opposite perspective: the defensive side of the game. In hopes of analyzing the game from a more concrete perspective, countless mathemeticians - most famously, Bill James - have developed numerous statistical models based on real life data of Major League Baseball (MLB) players. Large numbers of metrics go into these models, but what this project …