Machine Learning Paradigms Applications of Learning and Analytics in Intelligent Systems /

This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are stro...

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Detaylı Bibliyografya
Müşterek Yazar: SpringerLink (Online service)
Diğer Yazarlar: Tsihrintzis, George A. (Editör), Virvou, Maria (Editör), Sakkopoulos, Evangelos (Editör), Jain, Lakhmi C. (Editör)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edisyon:1st ed. 2019.
Seri Bilgileri:Learning and Analytics in Intelligent Systems, 1
Konular:
Computational intelligence.
Engineering > Data processing.
Machine learning.
Computational Intelligence.
Data Engineering.
Machine Learning.
Online Erişim:Full-text access
OPAC'ta görüntüle
İçindekiler:
  • Chapter 1: Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems
  • Chapter 2: A Comparison of Machine Learning Techniques to Predict the Risk of Heart Failure
  • Chapter 3: Differential gene Expression Analysis of RNA-seq Data Using Machine Learning for Cancer Research
  • Chapter 4: Machine Learning Approaches for Pap-Smear Diagnosis: An Overview
  • Chapter 5: Multi-Kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems
  • Chapter 6: Conceptualizing and Measuring Energy Security: Geopolitical Dimensions, Data Availability, Quantitative and Qualitative Methods
  • Chapter 7: Automated Stock Price Motion Prediction Using Technical Analysis Datasets and Machine Learning
  • Chapter 8: Airport Data Analysis Using Common Statistical Methods and Knowledge-Based Techniques
  • Chapter 9: A Taxonomy and Review of the Network Data Envelopment Analysis Literature
  • Chapter 10: Applying Advanced Data Analytics and Machine Learning to Enhance the Safety Control of Dams
  • Chapter 11: Analytics and Evolving Landscape of Machine Learning for Emergency Response
  • Chapter 12: Social Media Analytics, Types and Methodology
  • Chapter 13: Machine Learning Methods for Opinion Mining in Text: The Past and the Future
  • Chapter 14: Ship Detection Using Machine Learning and Optical Imagery in the Maritime Environment
  • Chapter 15: Video Analytics for Visual Surveillance and Applications: An Overview and Survey
  • Chapter 16: Machine Learning in Alternate Testing of Integrated Circuits.

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