Ensemble Machine Learning : Methods and Applications

Annotation 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 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 detection and are now being applied in areas as diverse as object trackingand 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 various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike Front Matter....Pages i-viii Ensemble Learning....Pages 1-34 Boosting Algorithms: A Review of Methods, Theory, and Applications....Pages 35-85 Boosting Kernel Estimators....Pages 87-115 Targeted Learning....Pages 117-156 Random Forests....Pages 157-175 Ensemble Learning by Negative Correlation Learning....Pages 177-201 Ensemble Nyström....Pages 203-223 Object Detection....Pages 225-250 Classifier Boosting for Human Activity Recognition....Pages 251-272 Discriminative Learning for Anatomical Structure Detection and Segmentation....Pages 273-306 Random Forest for Bioinformatics....Pages 307-323 Back Matter....Pages 325-329 The primary goal of this book is to give readers a complete treatment of the state-of-the-art ensemble learning methods. It also provides a set of applications that demonstrate the various usages of ensemble learning methods in the real-world.