Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- AIC (1)
- ANOVA (1)
- AdaBoost (1)
- Atlantic Sturgeon (1)
- Dimension reduction (1)
-
- Dimensionality reduction (1)
- EDNA (1)
- Environmental DNA (1)
- GMIFS (1)
- Gene expression (1)
- Goodness-of-Fit Testing (1)
- Graph-based Regularization (1)
- Hematopoietic stem cell transplantation (1)
- Heterogeneity of Vaciance (1)
- High-throughput sequencing (1)
- Intrinsic heterogeneity (1)
- Kruskal-Wallis (1)
- L1-norm regularization (1)
- Leaky integrate-and-fire (1)
- Machine Learning (1)
- Maximum Likelihood (1)
- Micronuclei (1)
- Mixed integer programming (1)
- Model Selection (1)
- Model-Independent Error Estimate (1)
- Monte Carlo simulations (1)
- Network heterogeneity (1)
- Optimal Design of Experiments (1)
- Optimization (1)
- PCR (1)
- Publication
- Publication Type
Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
The Support Vector Machine And Mixed Integer Linear Programming: Ramp Loss Svm With L1-Norm Regularization, Eric J. Hess, J. Paul Brooks
The Support Vector Machine And Mixed Integer Linear Programming: Ramp Loss Svm With L1-Norm Regularization, Eric J. Hess, J. Paul Brooks
Statistical Sciences and Operations Research Publications
The support vector machine (SVM) is a flexible classification method that accommodates a kernel trick to learn nonlinear decision rules. The traditional formulation as an optimization problem is a quadratic program. In efforts to reduce computational complexity, some have proposed using an L1-norm regularization to create a linear program (LP). In other efforts aimed at increasing the robustness to outliers, investigators have proposed using the ramp loss which results in what may be expressed as a quadratic integer programming problem (QIP). In this paper, we consider combining these ideas for ramp loss SVM with L1-norm regularization. The result is four …
Principal Component Analysis And Optimization: A Tutorial, Robert Reris, J. Paul Brooks
Principal Component Analysis And Optimization: A Tutorial, Robert Reris, J. Paul Brooks
Statistical Sciences and Operations Research Publications
No abstract provided.
Firing Rate Dynamics In Recurrent Spiking Neural Networks With Intrinsic And Network Heterogeneity, Cheng Ly
Firing Rate Dynamics In Recurrent Spiking Neural Networks With Intrinsic And Network Heterogeneity, Cheng Ly
Statistical Sciences and Operations Research Publications
Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, …
Realistic Spiking Neuron Statistics In A Population Are Described By A Single Parametric Distribution, Lauren Crow 9370373
Realistic Spiking Neuron Statistics In A Population Are Described By A Single Parametric Distribution, Lauren Crow 9370373
Undergraduate Research Posters
The spiking of activity of neurons throughout the cortex is random and complicated. This complicated activity requires theoretical formulations in order to understand the underlying principles of neural processing. A key aspect of theoretical investigations is characterizing the probability distribution of spiking activity. This study aims to better understand the statistics of the time between spikes, or interspike interval, in both real data and a spiking model with many time scales. Exploration of the interspike intervals of neural network activity can provide a better understanding of neural responses to different stimuli. We consider different parametric distribution fitting techniques to characterize …
Graph-Based Regularization In Machine Learning: Discovering Driver Modules In Biological Networks, Xi Gao
Graph-Based Regularization In Machine Learning: Discovering Driver Modules In Biological Networks, Xi Gao
Theses and Dissertations
Curiosity of human nature drives us to explore the origins of what makes each of us different. From ancient legends and mythology, Mendel's law, Punnett square to modern genetic research, we carry on this old but eternal question. Thanks to technological revolution, today's scientists try to answer this question using easily measurable gene expression and other profiling data. However, the exploration can easily get lost in the data of growing volume, dimension, noise and complexity. This dissertation is aimed at developing new machine learning methods that take data from different classes as input, augment them with knowledge of feature relationships, …
Controlling For Confounding When Association Is Quantified By Area Under The Roc Curve, Hadiza I. Galadima
Controlling For Confounding When Association Is Quantified By Area Under The Roc Curve, Hadiza I. Galadima
Theses and Dissertations
In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not done randomly in observational studies, comparisons of outcomes between exposed and non-exposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of odds ratio and hazard ratio. However, there is a lack of research into the performance of propensity score methods for estimating the …
High-Throughput Data Analysis: Application To Micronuclei Frequency And T-Cell Receptor Sequencing, Mateusz Makowski
High-Throughput Data Analysis: Application To Micronuclei Frequency And T-Cell Receptor Sequencing, Mateusz Makowski
Theses and Dissertations
The advent of high-throughput sequencing has brought about the creation of an unprecedented amount of research data. Analytical methodology has not been able to keep pace with the plethora of data being produced. Two assays, ImmunoSEQ and the cytokinesisblock micronucleus (CBMN), that both produce count data and have few methods available to analyze them are considered.
ImmunoSEQ is a sequencing assay that measures the beta T-cell receptor (TCR) repertoire. The ImmunoSEQ assay was used to describe the TCR repertoires of patients that have undergone hematopoietic stem cell transplantation (HSCT). Several different methods for spectratype analysis were extended to the TCR …
Considerations For Screening Designs And Follow-Up Experimentation, Robert D. Leonard
Considerations For Screening Designs And Follow-Up Experimentation, Robert D. Leonard
Theses and Dissertations
The success of screening experiments hinges on the effect sparsity assumption, which states that only a few of the factorial effects of interest actually have an impact on the system being investigated. The development of a screening methodology to harness this assumption requires careful consideration of the strengths and weaknesses of a proposed experimental design in addition to the ability of an analysis procedure to properly detect the major influences on the response. However, for the most part, screening designs and their complementing analysis procedures have been proposed separately in the literature without clear consideration of their ability to perform …
Proof-Of-Concept Of Environmental Dna Tools For Atlantic Sturgeon Management, Jameson Hinkle
Proof-Of-Concept Of Environmental Dna Tools For Atlantic Sturgeon Management, Jameson Hinkle
Theses and Dissertations
Abstract
The Atlantic Sturgeon (Acipenser oxyrinchus oxyrinchus, Mitchell) is an anadromous species that spawns in tidal freshwater rivers from Canada to Florida. Overfishing, river sedimentation and alteration of the river bottom have decreased Atlantic Sturgeon populations, and NOAA lists the species as endangered. Ecologists sometimes find it difficult to locate individuals of a species that is rare, endangered or invasive. The need for methods less invasive that can create more resolution of cryptic species presence is necessary. Environmental DNA (eDNA) is a non-invasive means of detecting rare, endangered, or invasive species by isolating nuclear or mitochondrial DNA (mtDNA) from the …
Comparing Welch's Anova, A Kruskal-Wallis Test And Traditional Anova In Case Of Heterogeneity Of Variance, Hangcheng Liu
Comparing Welch's Anova, A Kruskal-Wallis Test And Traditional Anova In Case Of Heterogeneity Of Variance, Hangcheng Liu
Theses and Dissertations
Analysis of variance (ANOVA) is a robust test against the normality assumption, but it may be inappropriate when the assumption of homogeneity of variance has been violated. Welch ANOVA and the Kruskal-Wallis test (a non-parametric method) can be applicable for this case. In this study we compare the three methods in empirical type I error rate and power, when heterogeneity of variance occurs and find out which method is the most suitable with which cases including balanced/unbalanced, small/large sample size, and/or with normal/non-normal distributions.