A statistic is called a sufficient statistic for a parameter if the statistic captures all the information about the parameter, contained in the data. More generally, a statistic is called a sufficient statistic for learning a hypothesis using a particular learning algorithm applied to a given dataset if there exists an algorithm that takes as input the statistic and outputs the desired hypothesis. A query that returns a statistic is called a statistical query.
Published in Chapter:
Learning Classifiers from Distributed Data Sources
Doina Caragea (Kansas State University, USA) and Vasant Honavar (Iowa State University, USA)
Copyright: © 2009
|Pages: 8
DOI: 10.4018/978-1-60566-242-8.ch063
Abstract
Recent development of high throughput data acquisition technologies in a number of domains (e.g., biological sciences, atmospheric sciences, space sciences, commerce) together with advances in digital storage, computing, and communications technologies have resulted in the proliferation of a multitude of physically distributed data repositories created and maintained by autonomous entities (e.g., scientists, organizations). The resulting increasingly data-rich domains offer unprecedented opportunities in computer assisted data-driven knowledge acquisition in a number of applications, including, in particular, data-driven scientific discovery, data-driven decision-making in business and commerce, monitoring and control of complex systems, and security informatics.