the goal of this work is the fast extraction of relevant information from document images. Examples of interesting information are the type of document (e.g. form, report, letter), the title of an article or the sende...
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this book constitutes the refereed proceedings of the 6thinternationalconference on patternrecognition in bioinformatics, PRIB 2011, held in Delft, the Netherlands, in November 2011. the 29 revised full papers pres...
详细信息
ISBN:
(数字)9783642248559
ISBN:
(纸本)9783642248542
this book constitutes the refereed proceedings of the 6thinternationalconference on patternrecognition in bioinformatics, PRIB 2011, held in Delft, the Netherlands, in November 2011. the 29 revised full papers presented were carefully reviewed and selected from 35 submissions. the papers cover the wide range of possible applications of bioinformatics in patternrecognition: novel algorithms to handle traditional patternrecognition problems such as (bi)clustering, classification and feature selection; applications of (novel) patternrecognition techniques to infer and analyze biological networks and studies on specific problems such as biological image analysis and the relation between sequence and structure. they are organized in the following topical sections: clustering, biomarker selection and classification, network inference and analysis, image analysis, and sequence, structure, and interactions.
pattern classification tasks in digital pathology, which involves the analysis of high resolution digital slides of tissue samples for medical diagnosis, are, like many other medical decision making processes, often i...
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ISBN:
(纸本)9781538610237
pattern classification tasks in digital pathology, which involves the analysis of high resolution digital slides of tissue samples for medical diagnosis, are, like many other medical decision making processes, often imbalanced. this means that there are (many) more training samples of some classes available compared to others, while it is often the minority class(es) that are of medical interest. In this paper, we present strategies for addressing class imbalance in pattern classification problems including the development of cost-sensitive fuzzy classifiers and the derivation of ensemble classification methods, that is classifiers that employ multiple predictors, dedicated for imbalanced classification.
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