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£100.08
Springer Feature Learning and Understanding Algorithms Book
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Description
Key Features
Covers traditional feature learning methods including principal component analysis and linear discriminant analysis for foundational knowledge.
Explores advanced techniques such as sparse learning and low-rank decomposition to handle modern data challenges.
Provides deep insights into geometrical-structure-based methods and tensor-based approaches for complex data analysis.
Combines theoretical analysis with practical case studies to bridge the gap between concept and application.
Focuses on the importance of feature selection and learning as the primary driver of model effectiveness.
Product Specifications
- Brand
- Springer
- Format
- paperback
- ASIN
- 3030407969
- Domain
- Amazon UK
- Release Date
- 04 April 2021
- Listed Since
- 08 March 2021
Barcode
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