About the Book:
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 detection and are now being applied in areas as diverse as object tracking and bioinformatics.
Table of Contents
Ensemble LearningBoosting Algorithms: A Review of Methods, Theory, and ApplicationsBoosting Kernel EstimatorsTargeted LearningRandom ForestsEnsemble Learning by Negative Correlation LearningObject DetectionDiscriminative Learning for Anatomical Structure Detection and Segmentation
