£111.92

Springer - The First Discriminant Theory of Linearly Separable Data

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Description

Explore advanced computational concepts with The First Discriminant Theory of Linearly Separable Data by Springer. This academic work covers a wide range of practical applications, moving from foundational concepts like exams and medical diagnoses with misclassifications to complex biological data analysis involving 169 microarrays for cancer gene diagnosis. Designed for readers interested in computing and internet databases, this book provides a specialized look at how data separation theories function in real-world scenarios. Whether you are studying the basics of database logic or looking into specific medical diagnostic models, this text offers a structured approach to understanding linearly separable data. It serves as a valuable resource for students and professionals seeking to understand how mathematical theories translate into practical tools for medical and computational fields.

Key Features

Covers diverse applications ranging from simple exam results to complex medical diagnosis scenarios.

Provides specific analysis regarding 169 microarrays used in cancer gene diagnosis.

Explores the theory of linearly separable data and its impact on misclassification issues.

Published by Springer, a leading name in academic and professional technical literature.

Suitable for those studying computing, internet databases, and introductory data science.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
13 April 2024
Listed Since
21 November 2023

Barcode

No barcode data available

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