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Entropy Randomization in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
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Entropy Randomization in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Entropy Randomization in Machine Learning presents a new approach to machine learning―entropy randomization―to obtain optimal solutions under uncertainty.
Entropy Randomization in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
物品 #: 91908792

Entropy Randomization in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

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物品 #: 91908792

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Entropy Randomization in Machine Learning presents a new approach to machine learning―entropy randomization―to obtain optimal solutions under uncertainty.
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What Stands Out

In-Depth Analysis
This book offers a comprehensive exploration of entropy randomization techniques, catering to both beginners and seasoned practitioners in machine learning, enhancing understanding of complex concepts.
Cutting-Edge Research
Presents the latest findings and methodologies in entropy randomization, providing readers with advanced insights that can be applied to contemporary machine learning challenges and projects.
Practical Applications
Includes real-world examples and applications, allowing readers to see how entropy randomization can improve model performance and address specific problems in machine learning effectively.

產品詳情

Shop Entropy Randomization in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition) online at a best price in 香港. 1032306289
Publisher Chapman and Hall/CRC
Publication date August 9, 2022
Edition 1st
Language English
Print length 392 pages
ISBN-10 1032306289
ISBN-13 978-1032306285
Item Weight 1.58 pounds (720 grams)
Dimensions 6.38 x 1.1 x 9.45 inches (16.2 x 2.8 x 24 cm)

產品敘述

Entropy Randomization in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

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HKD 1114

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特色和優勢

  • Presents innovative entropy randomization methods for optimal machine learning solutions.
  • Addresses uncertainty in data and models for various applications.
  • Includes theoretical foundations and practical applications in multiple fields.
  • Offers new numerical methods for random optimization and integrating multidimensional data.
  • Targets undergraduates, postgraduates, researchers, and engineers in AI and machine learning.
  • A systematic guide from data processing to real-world problem solutions.

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