TY  - BOOK
AU  - Criminisi,Antonio
AU  - Shotton,Jamie
AU  - Konukoglu,Ender
TI  - Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning 
T2  - Foundations and trends in computer graphics and vision,
SN  - 9781601985415 (electronic)
AV  - QA166.2 .C753 2012
U1  - 511/.52 23
PY  - 2012///
CY  - Hanover, Mass.
PB  - Now Publishers
KW  - Decision trees
KW  - Expert systems (Computer science)
KW  - Design
KW  - Decision support systems
KW  - Logic programming
KW  - Machine learning
KW  - Decision making
N1  - Includes bibliographical references (p. 221-227); 1. Overview and scope -- 2. The random decision forest model -- 3. Classification forests -- 4. Regression forests -- 5. Density forests -- 6. Manifold forests -- 7. Semi-supervised forests -- 8. Random ferns and other forest variants -- Appendix A. Deriving the regression information gain -- Acknowledgements; Restricted to subscribers or individual document purchasers; Google Scholar; Google Book Search; INSPEC; Scopus; ACM Computing Guide; DBPLP Computer Science Bibliography; Zentralblatt MATH Database; AMS MathSciNet; ACM Computing Reviews; Antonio Criminisi, Jamie Shotton and Ender Konukoglu (2012) "Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning", Foundations and Trendsʼ in Computer Graphics and Vision: Vol. 7: No 2-3, pp 81-227; Also available in print
N2  - This review presents a unified, efficient model of random decision forests which can be applied to a number of machine learning, computer vision, and medical image analysis tasks.  Our model extends existing forest-based techniques as it unifies classification, regression, density estimation, manifold learning, semi-supervised learning, and active learning under the same decision forest framework. This gives us the opportunity to write and optimize the core implementation only once, with application to many diverse tasks.  The proposed model may be used both in a discriminative or generative way and may be applied to discrete or continuous, labeled or unlabeled data; The main contributions of this review are: (1) Proposing a unified, probabilistic and efficient model for a variety of learning tasks; (2) Demonstrating margin-maximizing properties of classification forests; (3) Discussing probabilistic regression forests in comparison with other nonlinear regression algorithms; (4) Introducing density forests for estimating probability density functions; (5) Proposing an efficient algorithm for sampling from a density forest; (6) Introducing manifold forests for nonlinear dimensionality reduction; (7) Proposing new algorithms for transductive learning and active learning. Finally, we discuss how alternatives such as random ferns and extremely randomized trees stem from our more general forest model
UR  - http://dx.doi.org/10.1561/0600000035
ER  -