Robust classification of signal estimates given a channel model

Parrish, Nathan; Gupta, Manisha; Anderson, Hyrum S.

doi:10.1109/ssp.2011.5967679

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A Different Classification Strategies1

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2014

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“…However, the latter leads to a loss of information which may in certain cases be of high importance: classification is done on the sequence of intermediate estimations, leaving out possible sources of variability and correlation between hierarchy levels. Note that the ''sequential approach'' is not limited to the logistic regression since it is as well used for classification for example in proteomics [35], in chemical out-of-specification tests [36], in classification of corrupted signals [37]; partition methods such as fuzzy c-means [38] can easily be adapted to integrate a sequential classification.…”

Section: A Different Classification Strategiesmentioning

confidence: 99%

Classification of Proteomic MS Data as Bayesian Solution of an Inverse Problem

et al. 2014

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International audienceThe cells in an organism emit different amounts of proteins according to their clinical state (healthy/pathological, for instance). The resulting proteomic profile can be used for early detection, diagnosis, and therapy planning. In this paper, we study the classification of a proteomic sample from the point of view of an inverse problem with a joint Bayesian solution, called inversion-classification. We propose a hierarchical physical forward model and present encouraging results from both simulation and clinical data

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Section: A Different Classification Strategiesmentioning

confidence: 99%