000 04425nam a2200577 a 4500
001 610600000035
006 m e d
007 cr cn |||m|||a
008 120416s2012 maua fsab 000 0 eng d
020 _a9781601985415 (electronic)
020 _z9781601985408 (print)
024 7 _a10.1561/0600000035
_2doi
040 _aCaBNVSL
_cCaBNVSL
_dCaBNVSL
050 4 _aQA166.2
_b.C753 2012
082 0 4 _a511/.52
_223
035 _a(CaBNVSL)slc00228586
100 1 _aCriminisi, Antonio,
_d1972-
_995589
245 1 0 _aDecision forests
_h[electronic resource] :
_ba unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning /
_cAntonio Criminisi, Jamie Shotton, and Ender Konukoglu.
246 3 0 _aUnified framework for classification, regression, density estimation, manifold learning and semi-supervised learning.
260 _aHanover, Mass. :
_bNow Publishers,
_cc2012.
300 _a1 electronic text ([81]-227 p.) :
_bill. (some col.), digital file.
490 1 _aFoundations and trends in computer graphics and vision,
_x1572-2759 ;
_vv. 7, issue 2-3, p. 81-227
504 _aIncludes bibliographical references (p. 221-227).
505 0 _a1. 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.
506 _aRestricted to subscribers or individual document purchasers.
510 0 _aGoogle Scholar
510 0 _aGoogle Book Search
510 0 _aINSPEC
510 0 _aScopus
510 0 _aACM Computing Guide
510 0 _aDBPLP Computer Science Bibliography
510 0 _aZentralblatt MATH Database
510 0 _aAMS MathSciNet
510 0 _aACM Computing Reviews
520 3 _aThis 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.
520 8 _aThe 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.
524 _aAntonio 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.
530 _aAlso available in print.
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
588 _aTitle from PDF (viewed on 16 April 2012).
650 0 _aDecision trees.
_928270
650 0 _aExpert systems (Computer science)
_xDesign.
_995590
650 0 _aDecision support systems.
_917448
650 0 _aLogic programming.
_96178
650 0 _aMachine learning
_xDecision making.
_995591
700 1 _aShotton, Jamie.
_995592
700 1 _aKonukoglu, Ender.
_995593
830 0 _aFoundations and trends in computer graphics and vision (Online),
_x1572-2759 ;
_vv. 7, issue 2-3, p. 81-227.
_995594
856 4 8 _3Abstract with links to full text:
_uhttp://dx.doi.org/10.1561/0600000035
999 _c392375
_d392375