information theoretic approach
1Information theory — Not to be confused with Information science. Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental… …
2Akaike information criterion — Akaike s information criterion, developed by Hirotsugu Akaike under the name of an information criterion (AIC) in 1971 and proposed in Akaike (1974), is a measure of the goodness of fit of an estimated statistical model. It is grounded in the… …
3Akaike Information Criterion — Ein Informationskriterium ist ein Kriterium zur Auswahl eines Modells in der angewandten Statistik bzw. der Ökonometrie. Dabei gehen die Anpassungsgüte des geschätzten Modells an die vorliegenden empirischen Daten (Stichprobe) und Komplexität des …
4Information society — For other uses, see Information society (disambiguation). The aim of the information society is to gain competitive advantage internationally through using IT in a creative and productive way. An information society is a society in which the… …
5Extreme physical information — (EPI) is a principle, first described and formulated in 1998 Frieden, B. Roy Physics from Fisher Information: A Unification , 1st Ed. Cambridge University Press, ISBN 0 521 63167 X, pp328, 1998 ( [ref name= Frieden6 ] shows 2nd Ed.)] by B. Roy… …
6Decision-theoretic rough sets — (DTRS) is a probabilistic extension of rough set classification. First created in 1990 by Dr. Yiyu Yao[1], the extension makes use of loss functions to derive and region parameters. Like rough sets, the lower and upper approximations of a set are …
7Determining the number of clusters in a data set — Determining the number of clusters in a data set, a quantity often labeled k as in the k means algorithm, is a frequent problem in data clustering, and is a distinct issue from the process of actually solving the clustering problem. For a certain …
8Model selection — is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is …
9Kullback–Leibler divergence — In probability theory and information theory, the Kullback–Leibler divergence[1][2][3] (also information divergence, information gain, relative entropy, or KLIC) is a non symmetric measure of the difference between two probability distributions P …
10Minimum description length — The minimum description length (MDL) principle is a formalization of Occam s Razor in which the best hypothesis for a given set of data is the one that leads to the best compression of the data. MDL was introduced by Jorma Rissanen in 1978. It is …
