Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning Antonio Criminisi

ISBN: 9781601985408

Published: April 4th 2012

Paperback

162 pages


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Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning  by  Antonio Criminisi

Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning by Antonio Criminisi
April 4th 2012 | Paperback | PDF, EPUB, FB2, DjVu, talking book, mp3, ZIP | 162 pages | ISBN: 9781601985408 | 6.16 Mb

In recent years decision forests have established themselves as one of the most promising techniques in machine learning, computer vision and medical image analysis. This book is directed at engineers and PhD students who wish to learn the basics ofMoreIn recent years decision forests have established themselves as one of the most promising techniques in machine learning, computer vision and medical image analysis.

This book is directed at engineers and PhD students who wish to learn the basics of decision forests as well as more senior researchers who wish to push the state of the art in automated image understanding. The authors presents a unified, efficient model of random decision forests which can be used in a number of applications such as scene recognition from photographs, object recognition in images, automatic diagnosis from radiological scans and document analysis. Such applications have traditionally been addressed by different, supervised or unsupervised machine learning techniques.

In contrast, here we cast diverse tasks such as regression, classification and semi-supervised learning as instances of the same general decision forest model. The flexibility of the forest framework further extends to tasks such as density estimation, manifold learning and semi-supervised learning. The unified forest framework gives us the opportunity to implement and optimize the underlying algorithm only once, and then easily adapt it to individual applications with relatively small changes. The theoretical basis and numerous explanatory examples presented in this book serve as a solid platform upon which to build exciting future research.



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