Buch, Englisch, 332 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 517 g
Methods and Applications
Buch, Englisch, 332 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 517 g
ISBN: 978-1-4899-8817-1
Verlag: Springer
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics.
Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik Mathematik Stochastik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
Weitere Infos & Material
Introduction of Ensemble Learning.- Boosting Algorithms: Theory, Methods and Applications.- On Boosting Nonparametric Learners.- Super Learning.- Random Forest.- Ensemble Learning by Negative Correlation Learning.- Ensemble Nystrom Method.- Object Detection.- Ensemble Learning for Activity Recognition.- Ensemble Learning in Medical Applications.- Random Forest for Bioinformatics.
