MACHINE LEARNING FOR INFORMATION EXTRACTION

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" MACHINE LEARNING FOR INFORMATION EXTRACTION "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	149733
Doc. No	:	ET21525
Main Entry	:	DMITRY ZELENKO
Title Proper	:	MACHINE LEARNING FOR INFORMATION EXTRACTION
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	Learning is the process of estimating unknown dependencies from observations. The concept oflearning has been studied and formalized in different fields, viz., philosophy, psychology, cognitivescience, statistics, pattern recognition, and computer science. In this section, we survey quantitativeformalizations of the process of learning and show how they converge and give rise to the modernconceptual framework of statistical and computational learning theory.Let X be a (measurable) set of possible observations. The underlying assumption in mostlearning models is that there exists a fixed unknown probability distribution P over X. Thelearning process receives a set S observations from X sampled independently according to P andseeks to estimate an unknown dependency from the finite sample S.tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL44697.pdf
Title and statement of responsibility and	:	MACHINE LEARNING FOR INFORMATION EXTRACTION [Thesis]