£135.00

CRC Press Manifold Learning Theory and Applications Book

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Description

Engineers and scientists working with large volumes of high-dimensional data often face the challenge of isolating meaningful low-dimensional structures from complex observations. Manifold Learning Theory and Applications provides the necessary tools to solve these dimensionality reduction issues. This text addresses a specific void in current literature by presenting state-of-the-art techniques used to extract actionable information from data. This book is designed for professionals and researchers who need to master groundbreaking techniques for pattern recognition and image processing. By understanding manifold learning, readers can better navigate the complexities of neural networks, machine learning, and computer vision. Whether you are working on high-dimensional data analysis or developing new algorithms, this resource offers the theoretical foundation and practical applications required for modern data science and engineering tasks.

Key Features

Addresses the challenge of isolating meaningful low-dimensional structures from high-dimensional observations.

Provides state-of-the-art techniques in manifold learning to aid in effective dimensionality reduction.

Supports practical applications in machine learning, neural networks, and pattern recognition.

Offers specialized knowledge for fields including image processing and computer vision.

Fills a significant gap in existing literature for engineers and scientists working with large data volumes.

Product Specifications

Format
Hardcover
Domain
Amazon UK
Release Date
06 January 2012
Listed Since
08 July 2011

Barcode

No barcode data available

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