Constraint in information theory Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Historically, information theory was developed by Claude E. Shannon to find fundamental limits on compressing and reliably storing and communicating data. Since its inception it has broadened to find applications in many refers to the degree of statistical dependence between or among variables.
Garner[1] provides a thorough discussion of various forms of constraint (internal constraint, external constraint, total constraint) with application to pattern recognition Pattern recognition is a sub-topic of machine learning. It is "the act of taking in raw data and taking an action based on the category of the data".[citation needed] Most research in pattern recognition is about methods for supervised learning and unsupervised learning and psychology Psychology is an academic and applied discipline involving the systematic, and often scientific, study of human/animal mental functions and behavior. Occasionally, in addition or opposition to employing the scientific method, it also relies on symbolic interpretation and critical analysis, although it often does so less prominently than other.
See also
References
- ^ Garner W R (1962). Uncertainty and Structure as Psychological Concepts, John Wiley & Sons, New York.
Categories: Information theory |
