Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Ensemble Machine Learning: Methods and Applications

Ensemble Machine Learning: Methods and Applications

Zhang, Cha
Ma, Yunqian

Hardcover

Technology & EngineeringDatabasesGeneral Computers

ISBN10: 1441993258
ISBN13: 9781441993250
Publisher: Springer
Published: Feb 17 2012
Pages: 332
Weight: 1.44
Height: 0.81 Width: 6.14 Depth: 9.21
Language: English

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.

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