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Pattern recognition is a term for a technology that recognizes and analyses patterns automatically by machine and which has been used successfully in many areas of application including optical character recognition. Speech recognition, remote sensing and medical imaging processing. Because 'recognition' is almost synonymous with 'classification' in this field, pattern recognition includes statistical classification techniques such as discriminant analysis (here known as supervised pattern recognition or supervised learning) and cluster analysis (known as unsupervised pattern recognition or unsupervised learning). Pattern recognition is closely related to artificial intelligence, artificial neural networks and machine learning and is one of the main techniques used in data mining. Perhaps the distinguishing feature of pattern recognition is that no direct analogy is made in its methodology to underlying biological processes.
Atomistic fallacy : A fallacy which arises because of the association between two variables at the individual level might vary from the association between the same two variables m
Difference between tretment design and experimental design
Cluster randomization : The random allocation of the groups or clusters of the individuals in the formation of treatment groups.Eeven though not as statistically ef?cient as the in
how to resolve sequencing problem if jobs 6 given and 4 machines given. how to apply johnson rule for making to machines under this conditions. please give solution as soon as poss
A metal fabrication process uses a die-cast metal fastener at a uniform rate of 300 units per year. Currently, this item is currently purchased from an external supplier at a unit
Opreation research phase
The scatter plot of SRES1 versus totexp demonstrates that there is non-linear relationship that exists as most of the points are below and above zero. The scatter plot show that th
stationary time series
Non linear mapping (NLM ) is a technique for obtaining a low-dimensional representation of the set of multivariate data, which operates by minimizing a function of the differences
Goodmanand kruskal measures of association is the measures of associations which are useful in the situation where two categorical variables cannot be supposed to be derived from
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