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Full-Text Articles in Physical Sciences and Mathematics
Clustering Of Multiple Instance Data., Andrew D. Karem
Clustering Of Multiple Instance Data., Andrew D. Karem
Electronic Theses and Dissertations
An emergent area of research in machine learning that aims to develop tools to analyze data where objects have multiple representations is Multiple Instance Learning (MIL). In MIL, each object is represented by a bag that includes a collection of feature vectors called instances. A bag is positive if it contains at least one positive instance, and negative if no instances are positive. One of the main objectives in MIL is to identify a region in the instance feature space with high correlation to instances from positive bags and low correlation to instances from negative bags -- this region is …
Development And Characterization Of An Inexpensive Single-Particle Fluorescence Spectrometer For Detection And Classification Of Pollen And Other Bioaerosols, Benjamin E. Swanson
Development And Characterization Of An Inexpensive Single-Particle Fluorescence Spectrometer For Detection And Classification Of Pollen And Other Bioaerosols, Benjamin E. Swanson
Electronic Theses and Dissertations
Atmospheric aerosols are ubiquitous throughout the Earth’s atmosphere and can be important with respect to environmental systems and human health. Pollen particles are a class of primary biological aerosol particles (PBAPs) that cost the United States billions of dollars a year in loss of productivity and healthcare costs due to allergy and respiratory effects. Traditional methods of pollen detection rely on collection and subsequent identification by visual microscopy, yet few measurement stations exist in the United States. As such, current pollen forecasting models have relatively high prediction uncertainty, especially in regions without sampling stations. Recently, laser-induced fluorescence instrumentation has been …
Data Patterns Discovery Using Unsupervised Learning, Rachel A. Lewis
Data Patterns Discovery Using Unsupervised Learning, Rachel A. Lewis
Electronic Theses and Dissertations
Self-care activities classification poses significant challenges in identifying children’s unique functional abilities and needs within the exceptional children healthcare system. The accuracy of diagnosing a child's self-care problem, such as toileting or dressing, is highly influenced by an occupational therapists’ experience and time constraints. Thus, there is a need for objective means to detect and predict in advance the self-care problems of children with physical and motor disabilities. We use clustering to discover interesting information from self-care problems, perform automatic classification of binary data, and discover outliers. The advantages are twofold: the advancement of knowledge on identifying self-care problems in …