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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.
How large would the sample need to be if we are to pick a 95% confidence level sample: (i) From a population of 70; (ii) From a population of 450; (iii) From a population of 1000;
Reinterviewing is the second interview for a sample of survey respondents in which questions of the original interview (or the subset of them) are repeated again. The same methods
The procedures for extracting the pattern in a series of observations when this is obscured by the noise. Basically any such technique or method separates the original series into
Catastrophe theory : A theory of how little is the continuous changes in the independent variables which can have unexpected, discontinuous effects on the dependent variables. Exam
Resentful demoralization is the possible phenomenon in the clinical trials and intervention studies in which comparison groups not attaining a perceived desirable treatment become
A term usually used for unobserved individual heterogeneity. Such variation is of main concern in the medical statistics particularly in the analysis of the survival times where ha
Observational study is the study in which the objective is to discover cause-and-effect relationships but in which it is not feasible to use the controlled experimentation, in th
Johnson''s Job Sequencing for n jobs and 2 machines
This is an approach to the modelling of time-frequency surfaces which consists of a Bayesian regularization scheme in which the prior distributions over the time-frequency coeffici
Introduction to Generalized Linear Models (GLM) We introduce the notion of GLM as an extension of the traditional normal-theory-based linear regression models. This will be very
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