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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.
a sequence of numbers consist of six 6''s seven 7''s eight 8''s nine 9''s ten 10''s what is the arithmetic mean?
The procedures used for determining how the quality of life is affected by the environment, in particular by factors such as air and solid wastes, water pollution, hazardous substa
Generalized poisson distribution: The probability distribution can be defined as follows: The distribution corresponds to the situation in which the values of the rand
A name sometimes given to the type of diagram generally used in meta-analysis, in which point estimates and confidence intervals are displayed for all the studies included in the a
Principal factor analysis is the method of factor analysis which is basically equivalent to a principal components analysis performed on reduced covariance matrix attained by repl
Completeness : A term applied to a statistic t when there is only one function of that the statistic which can have the given expected value. If, for instance, the one function of
This is extension of the EM algorithm which typically converges more slowly than EM in terms of the iterations but can be much faster in the whole computer time. The general idea o
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
Data theory is anxious with how observations are transformed into data which can be analyzed. Data are thus viewed as the theory laden in the sense that the observations can be giv
Zero-inflated Poisson regression is the model for count data with the excess zeros. It supposes that with probability p the only possible observation is 0 and with the probabilit
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