915 resultados para Supervised pattern recognition methods
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"Supported in part by the Department of Computer Science and the Atomic Energy Commission under contract US AEC AT(11-1)2118."
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Mode of access: Internet.
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Cover title.
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"COO-2118-0028."
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"C00-1018-1213"--Cover.
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Bibliography: leaf 25.
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"Contract US AEC AT(11-1)2118."
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"UIUC-ENG-R-75-2539."
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"January 1985."
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On cover, 1978 : NBS-EIA
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Thesis (Ph.D.)--University of Washington, 2016-06
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Infection frequently causes exacerbations of chronic obstructive pulmonary disease (COPD). Mannose-binding lectin (MBL) is a pattern-recognition receptor that assists in clearing microorganisms. Polymorphisms in the MBL2 gene reduce serum MBL levels and are associated with risk of infection. We studied whether the MBL2 codon 54 B allele affected serum MBL levels, admissions for infective exacerbation in COPD and disease susceptibility. Polymorphism frequency was determined by PCR-RFLP in 200 COPD patients and 104 smokers with normal lung function. Serum MBL was measured as mannan-binding activity in a subgroup of 82 stable COPD patients. Frequency of COPD admissions for infective exacerbation was ascertained for a 2-year period. The MBL2 codon 54 B allele reduced serum MBL in COPD patients. In keeping, patients carrying the low MBL-producing B allele had increased risk of admission for infective exacerbation (OR 4.9, P-corrected = 0.011). No association of MBL2 genotype with susceptibility to COPD was detected. In COPD, serum MBL is regulated by polymorphism at codon 54 in its encoding gene. Low MBL-producing genotypes were associated with more frequent admissions to hospital with respiratory infection, suggesting that the MBL2 gene is disease-modifying in COPD. MBL2 genotype should be explored prospectively as a prognostic marker for infection risk in COPD.
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We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.
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Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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Objective: To determine the frequency and pattern of methods of outcome assessment used in Australian physical rehabilitation environments. Design: Postal survey. Methods: A questionnaire on service type, staffing, numbers of adults treated and outcome measures used for 7 conditions related to injury and road trauma as well as stroke and neuromuscular disorders was sent to 973 services providing adult physical rehabilitation treatment. Results: Questionnaires were completed by 440 service providers for a response rate of 45%, similar to that reported in a recent European survey reported in this journal. A small number of measures were reported as in use by most respondents, while a large number of measures were used by a few respondents. Measures of physical changes were used more frequently than those of generic well-being or quality of life. Ease of use and reporting to other professionals were cited as the most important reasons in selection of outcome measures. Conclusion: This Australian-wide survey detected considerable heterogeneity in outcome measurement procedures used in rehabilitation environments. While the goal of measurement may vary between providers and differ between conditions, the results highlight opportunities for harmonization, benchmarking and measurement of health-related quality of life.