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Machine learning is a term which literally means the ability of a machine to recognize patterns which have occurred repetitively and to improve its performance based on the past experience. In essence this reduces to the study of computer algorithms improve automatically through experience. The computer program is said to learn from the past experience E with respect to some class of tasks T and performance gauge P, if its performance at tasks in T, as measured by P, gets improves with experience E. Machine learning is inherently a multidisciplinary field by making use of results and techniques from probability and statistics, information theory , computational complexity theory etc; it is closely related to the pattern recognition and artificial intelligence and is broadly used in modern data mining.
Link functions: The link function relates the linear predictor ηi to the expected value of the data. In classical linear models the mean and the linear predictor are identical
Back-projection: A term most often applied to the procedure for reconstructing plausible HIV incidence curves from the AIDS incidence data. The method or technique assumes that th
Grade of membership model: This is the general distribution free method for the clustering of the multivariate data in which only categorical variables are included. The model ass
The model for data containing continuous and categorical variables both.The categorical data are summarized by the contingency table and their marginal distribution, 182by the mult
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
Captures recapture sampling : Another approach to a census for estimating the size of population, which operates by sampling the population number of times, identifying the individ
A procedure whereby the collection of multiple sample units are combined in their entirety or in part, to form the new sample. One or more succeeding measurements are taken on the
Non linear model : A model which is non-linear in the parameters, for instance are Some such type of models can be converted into the linear models by linearization (the s
A family of the probability distributions of the form given as here θ is the parameter and a, b, c, d are the known functions. It includes the gamma distribution, normal dis
Weighted least squares is the method of estimation in which the estimates arise from minimizing the weighted sum of squares of the differences between response variable and its pr
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