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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.
The procedure in which the prior distribution is required in the application of Bayesian inference, it is determined from empirical evidence, namely same data for which the posteri
You may have the opportunity to buy some electronic components. These components may be reliable (1) or unreliable (2). The potential pro?ts are £10,000 if the components are rel
difference between histogram and historigram
Particlefilters is a simulation method for tracking moving target distributions and for reducing computational burden of the dynamic Bayesian analysis. The method uses a Markov ch
Collective risk models : The models applied to insurance portfolios which do not create direct reference to the risk characteristics of individual members of the portfolio when des
Suppose the graph G is n-connected, regular of degree n, and has an even number of vertices. Prove that G has a one-factor. Petersen's 2-factor theorem (Theorem 5.40 in the note
The method of summarizing the large amounts of data by forming the frequency distributions, scatter diagrams, histograms, etc., and calculating statistics like means variances and
Multi co linearity is the term used in the regression analysis to indicate situations where the explanatory variables are related by a linear function, making the inference of the
This is an alternative to the Newton-Raphson technique for optimization (finding out the minimum or the maximum) of some function, which includes replacing the matrix of second der
The risk of being able to recognize the respondent's confidential information in the data set. Number of approaches has been proposed to measure the disclosure risk some of which c
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