Empirical Approach to Machine Learning

This book provides a 'one-stop source' for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today's data-driven world. After an introduction to the fundamentals, the book discusses i...

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Detaylı Bibliyografya
Asıl Yazarlar: Angelov, Plamen P. (Yazar), Gu, Xiaowei (Yazar)
Müşterek Yazar: SpringerLink (Online service)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Cham : Springer International Publishing : 2019.
Imprint: Springer,
Edisyon:1st ed. 2019.
Seri Bilgileri:Studies in Computational Intelligence, 800
Konular:
Computational intelligence.
Pattern recognition systems.
Big data.
Data mining.
Dynamics.
Nonlinear theories.
Computational Intelligence.
Automated Pattern Recognition.
Big Data.
Data Mining and Knowledge Discovery.
Applied Dynamical Systems.
Online Erişim:Full-text access
İçindekiler:
  • Introduction
  • Part I: Theoretical Background
  • Brief Introduction to Statistical Machine Learning
  • Brief Introduction to Computational Intelligence
  • Part II: Theoretical Fundamentals of the Proposed Approach
  • Empirical Approach - Introduction
  • Empirical Fuzzy Sets and Systems
  • Anomaly Detection - Empirical Approach
  • Data Partitioning - Empirical Approach
  • Autonomous Learning Multi-Model Systems
  • Transparent Deep Rule-Based Classifiers
  • Part III: Applications of the Proposed Approach
  • Applications of Autonomous Anomaly Detection.

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