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Pattern recognition - Wikipedia, the free encyclopedia
Pattern recognition is "the act of taking in raw data and taking an action based on the category of the data".[citation needed] Most research in pattern recognition is about methods for supervised learning and unsupervised learning.
Pattern recognition aims to classify data (patterns) based either on a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. This is in contrast to pattern matching, where the pattern is rigidly specified.
Pattern matching - Wikipedia, the free encyclopedia
In computer science, pattern matching is the act of checking for the presence of the constituents of a given pattern. In contrast to pattern recognition, the pattern is rigidly specified. Such a pattern concerns conventionally either sequences or tree structures. Pattern matching is used to test whether things have a desired structure, to find relevant structure, to retrieve the aligning parts, and to substitute the matching part with something else.
A Ketogenic Diet Favorably Affects Serum Biomarkers for Cardiovascular Disease in Normal-Weight Men -- Sharman et al. 132 (7): 1879 -- Journal of Nutrition
The results suggest that a short-term ketogenic diet does not have a deleterious effect on CVD risk profile and may improve the lipid disorders characteristic of atherogenic dyslipidemia.
A ketogenic diet favorably affects serum biomarkers for cardiovascular disease in normal-weight men.
Sharman MJ, Kraemer WJ, Love DM, Avery NG, Gómez AL, Scheett TP, Volek JS.
J Nutr. 2002 Jul;132(7):1879-85.
PMID: 12097663
Low-density lipoprotein subclass patterns and lipoprotein response to a reduced-fat diet in men -- Dreon et al. 8 (1): 121 -- The FASEB Journal (free full text PDF available)
Low-density lipoprotein subclass patterns and lipoprotein response to a reduced-fat diet in men.\nDreon DM, Fernstrom HA, Miller B, Krauss RM.\nFASEB J. 1994 Jan;8(1):121-6.\nPMID: 8299884
Low-density lipoprotein subclass patterns and risk of myocardial infarction.[JAMA. 1988] - PubMed Result
The LDL subclass pattern characterized by a preponderance of small, dense LDL particles was significantly associated with a threefold increased risk of myocardial infarction, independent of age, sex, and relative weight. Plasma levels of high-density lipoprotein cholesterol were decreased, and levels of triglyceride, very low-density lipoproteins, and intermediate-density lipoproteins were increased in subjects with this LDL subclass pattern. Multivariate logistic regression analyses showed that both high-density lipoprotein cholesterol and triglyceride levels contributed to the risk associated with the small, dense LDL subclass pattern. Thus, the metabolic trait responsible for this LDL subclass pattern results in a set of interrelated lipoprotein changes that lead to increased risk of coronary heart disease.
:
Low-density lipoprotein subclass patterns and risk of myocardial infarction.
Austin MA, Breslow JL, Hennekens CH, Buring JE, Willett WC, Krauss RM.
JAMA. 1988 Oct 7;260(13):1917-21.
PMID: 3418853
Wikipatterns - Wiki Patterns
This site contains a toolbox of patterns and anti-patterns, and a guide to major stages of wiki adoption that explores patterns to apply at each stage. Beyond this site, there are many other additional resources.
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