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Full-Text Articles in Physical Sciences and Mathematics
Ip Algorithm Applied To Proteomics Data, Christopher Lee Green
Ip Algorithm Applied To Proteomics Data, Christopher Lee Green
Theses and Dissertations
Mass spectrometry has been used extensively in recent years as a valuable tool in the study of proteomics. However, the data thus produced exhibits hyper-dimensionality. Reducing the dimensionality of the data often requires the imposition of many assumptions which can be harmful to subsequent analysis. The IP algorithm is a dimension reduction algorithm, similar in purpose to latent variable analysis. It is based on the principle of maximum entropy and therefore imposes a minimum number of assumptions on the data. Partial Least Squares (PLS) is an algorithm commonly used with proteomics data from mass spectrometry in order to reduce the …
Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting
Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting
Faculty Publications
Classification problems with output class overlap create problems for standard neural network approaches. We present a modification of a simple feed-forward neural network that is capable of learning problems with output overlap, including problems exhibiting hierarchical class structures in the output. Our method of applying weakened implicit negatives to address overlap and ambiguity allows the algorithm to learn a large portion of the hierarchical structure from very incomplete data. Our results show an improvement of approximately 58% over a standard backpropagation network on the hierarchical problem.