Buch, Englisch, Band 21, 162 Seiten, Format (B × H): 156 mm x 234 mm
Reihe: Foundations and Trends® in Computer Graphics and Vision
A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning
Buch, Englisch, Band 21, 162 Seiten, Format (B × H): 156 mm x 234 mm
Reihe: Foundations and Trends® in Computer Graphics and Vision
ISBN: 978-1-60198-540-8
Verlag: Now Publishers
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 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.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Entscheidungstheorie, Sozialwahltheorie
Weitere Infos & Material
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 9: Conclusions. Appendix A. Acknowledgements. References
