Beschreibung
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 systems 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.
Inhaltsverzeichnis
1. Introduction of Ensemble Learning.- 2. Super Learning.- 3. Boosting Algorithms: Theory, Methods, and Applications.- 4. On Boosting Nonparametric Learners.- 5. Random Forest.- 6. Ensemble Nystrom Method.- 7. Ensemble Learning by Negative Correlation Learning.- 8. Object Detection.- 9. Real-time Human Pose Recognition in Parts from Single Depth Images.- 10. Ensemble Learning for Activity Recognition.- 11. Ensemble Learning in Medical Applications.- 12. Random Forest for Bioinformatics.
Autorenporträt
Dr. Zhang works for Microsoft. Dr. Ma works for Honeywell.
