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Introduction to Algorithms for Data Mining and Machine Learning

Xin-She Yang (Autor)

Buch | Softcover

188 Seiten

2019
Academic Press Inc (Verlag)
978-0-12-817216-2 (ISBN)

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Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).

1. Introduction2. Mathematical Foundations3. Data Fitting and Method of Least Squares4. Logistic Regression and PCA5. Data Mining6. Artificial Neural Networks7. Support Vector Machine8. Deep Learning

Erscheinungsdatum	01.07.2019
Verlagsort	San Diego
Sprache	englisch
Maße	152 x 229 mm
Gewicht	320 g
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
	Informatik ► Theorie / Studium ► Algorithmen
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Mathematik ► Analysis
	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
ISBN-10	0-12-817216-9 / 0128172169
ISBN-13	978-0-12-817216-2 / 9780128172162
Zustand	Neuware