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
Feature Learning and Understanding: Algorithms and Applications

Feature Learning and Understanding: Algorithms and Applications

Zhao, Haitao
Lai, Zhihui
Leung, Henry

Hardcover

Series: Information Fusion and Data Science

Technology & EngineeringGeneral ComputersGeneral Science

ISBN10: 3030407934
ISBN13: 9783030407933
Publisher: Springer Nature
Published: Apr 4 2020
Pages: 291
Weight: 1.34
Height: 0.75 Width: 6.14 Depth: 9.21
Language: English

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.

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