£99.71

Springer Feature Learning and Understanding Algorithms Book

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Description

In the field of machine learning, the quality of your features often determines the success of your models more than the classifiers themselves. This book from Springer provides a comprehensive guide to both traditional and cutting-edge feature learning methods. By combining theoretical analysis with practical case studies, it offers a deep look into how to extract meaningful information from complex data sets. Readers will explore foundational techniques like principal component analysis and linear discriminant analysis alongside advanced modern approaches. Whether you are studying geometrical-structure-based methods or more complex processes like sparse learning and low-rank decomposition, this text serves as a vital resource for understanding information fusion and data science. It is designed for those looking to master the strategies that drive effective model performance through superior feature engineering.

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

Format
paperback
Domain
Amazon UK
Release Date
04 April 2021
Listed Since
08 March 2021

Barcode

No barcode data available

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