Part I Methods.- Ch1 Introduction.- Ch2 Bagging Predictors.- Ch3 Random Forest.- Ch4 Basic Boosting (AdaBoost and its Variations).- Ch5 More on Boosting (AnyBoost, BrownBoost, MilBoost, etc).- Ch6 Stacked Generalization.- Ch7 Deep Neural Networks.- Part II Applications.- Ch8 Object Detection.- Ch9 Segmentation and Tracking using Random Forest.- Ch10 Generic Image Recognition and Information Retrieval.- Ch11 Medical Application and Bioinformatics.- Ch12 Data Mining Concept-Drifting Data Streams
Über den Autor
Dr. Zhang works for Microsoft. Dr. Ma works for Honeywell.
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.
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.
Covers all existing methods developed for ensemble learning
Presents overview and in-depth knowledge about ensemble learning
Discusses the pros and cons of various ensemble learning methods
Demonstrate how ensemble learning can be used with real world applications