Ensemble machine learning : methods and applications /
Annotation
| Corporate Author: | |
|---|---|
| Other Authors: | , , |
| Format: | Book |
| Language: | English |
| Published: |
New York :
Springer,
c2012
New York : [2012] |
| Subjects: |
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| 245 | 0 | 0 | |a Ensemble machine learning : |b methods and applications / |c Cha Zhang, Yunqian Ma, editors |
| 260 | |a New York : |b Springer, |c c2012 | ||
| 264 | 1 | |a New York : |b Springer, |c [2012] | |
| 300 | |a 1 online resource (viii, 329 p.) : |b illl | ||
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| 504 | |a Includes bibliographical references and index | ||
| 505 | 0 | 0 | |t Ensemble Learning -- |t Boosting Algorithms: A Review of Methods, Theory, and Applications -- |t Boosting Kernel Estimators -- |t Targeted Learning -- |t Random Forests -- |t Ensemble Learning by Negative Correlation Learning -- |t Ensemble Nyström -- |t Object Detection -- |t Classifier Boosting for Human Activity Recognition -- |t Discriminative Learning for Anatomical Structure Detection and Segmentation -- |t Random Forest for Bioinformatics |
| 505 | 0 | 0 | |t Ensemble Learning -- |t Boosting Algorithms: A Review of Methods, Theory, and Applications -- |t Boosting Kernel Estimators -- |t Targeted Learning -- |t Random Forests -- |t Ensemble Learning by Negative Correlation Learning -- |t Ensemble Nyström -- |t Object Detection -- |t Classifier Boosting for Human Activity Recognition -- |t Discriminative Learning for Anatomical Structure Detection and Segmentation -- |t Random Forest for Bioinformatics |
| 520 | 8 | |a Annotation |b 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 | |
| 650 | 0 | |a Ensemble learning (Machine learning) | |
| 650 | 0 | |a Machine learning | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics |2 bisacsh | |
| 650 | 7 | |a Ingénierie |2 eclas | |
| 650 | 7 | |a Machine learning |2 fast | |
| 653 | 4 | |a Computational Intelligence | |
| 653 | 4 | |a Computer science | |
| 653 | 4 | |a Data mining | |
| 653 | 4 | |a Engineering | |
| 655 | 4 | |a Electronic books | |
| 700 | 1 | |a MA, YUNQIAN |1 http://viaf.org/viaf/91055696 | |
| 700 | 1 | |a Ma, Yunqian | |
| 700 | 1 | |a Zhang, Cha |1 http://viaf.org/viaf/7155153 | |
| 700 | 1 | |a Zhang, Cha | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 776 | 0 | 8 | |i Print version: |t Ensemble machine learning |d New York : Springer, 2012 |w (DLC) 2012930830 |
| 776 | 0 | 8 | |i Print version: |t Ensemble machine learning |d New York : Springer, 2012 |w (DLC) 2012930830 |
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