Average Time Complexity of Decision Trees - Intelligent Systems Reference Library
Average Time Complexity of Decision Trees - Intelligent Systems Reference Library
hardback
Published:
4 August, 2011
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Description
Decision tree is a widely used form of representing algorithms and knowledge. Compact data models
and fast algorithms require optimization of tree complexity. This book is a research monograph on
average time complexity of decision trees. It generalizes several known results and considers a number of new problems.
The book contains exact and approximate algorithms for decision tree optimization, and bounds on minimum average time
complexity of decision trees. Methods of combinatorics, probability theory and complexity theory are used in the proofs as
well as concepts from various branches of discrete mathematics and computer science. The considered applications include
the study of average depth of decision trees for Boolean functions from closed classes, the comparison of results of the performance
of greedy heuristics for average depth minimization with optimal decision trees constructed by dynamic programming algorithm,
and optimization of decision trees for the corner point recognition problem from computer vision.
The book can be interesting for researchers working on time complexity of algorithms and specialists
in test theory, rough set theory, logical analysis of data and machine learning.More Details
| Type | Book |
|---|---|
| ISBN13 | 9783642226601 |
| ISBN10 | 3642226604 |
| Number Of Pages | 104 |
| Item Weight | 1000 g |
| Publisher / Reseller | Springer-Verlag Berlin and Heidelberg GmbH & Co. KG |
| Format | hardback |
| Edition | 2011 ed. |
Media Reviews
From the reviews:
“The book contains several previously known results on the average time complexity of decision trees for a number of new problems. The author presents bounds on the minimum average time complexity of decision trees. … In summary, the results presented in the book may be of interest for researchers in test theory, rough set theory, and logical analysis of data and machine learning. The book is suitable for a graduate or PhD course.” (Jerzy Martyna, Zentralblatt MATH, Vol. 1244, 2012)