By Topic

The complexity of information extraction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)

How difficult are decision problems based on natural data, such as pattern recognition? To answer this question, decision problems are characterized by introducing four measures defined on a Boolean functionfofNvariables: the implementation costC(f), the randomnessR(f), the deterministic entropyH(f), and the complexityK(f). The highlights and main results are roughly as follows,l) C(f) approx R(f) H(f) approx K(f), all measured in bits.2)Decision problems based on natural data are partially random (in the Kolmogorov sense) and have low entropy with respect to their dimensionality, and the relations between the four measures translate to lower and upper bounds on the cost of solving these problems.3)Allowing small errors in the implementation offsaves a lot in the iow entropy case but saves nothing in the high-entropy case. Iffis partially structured, the implementation cost is reduced substantially.

Published in:

Information Theory, IEEE Transactions on  (Volume:32 ,  Issue: 4 )