Detail E-Book
The Elements of Statistical Learning
Pengarang: Trevor Hastie
Edisi: 2
ISBN/ISSN: 9780387848570
Penerbit: Springer
Tahun Terbit: 2009
Tempat Terbit: New York, Amerika Serikat
Bahasa: English
No. Panggil: ELE
GMD: Electronic Resource
Catatan
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman is an academic reference textbook that explores the concepts, methods, and applications of statistical learning in data analysis and predictive modeling.
The book presents a variety of statistical and computational approaches for understanding patterns, relationships, and information contained in datasets. Its coverage includes supervised and unsupervised learning, linear regression, classification, regularization, kernel methods, and statistical model assessment and selection.
Furthermore, the book discusses various machine learning techniques, including neural networks, support vector machines, decision trees, boosting, random forests, and ensemble learning. It also covers graphical models, clustering, and high-dimensional data analysis.
The book emphasizes conceptual understanding of statistical methods and their applications in various fields, including medicine, biology, finance, marketing, and computer science.
Overall, this textbook is intended for advanced students, researchers, academics, statisticians, and data science practitioners who wish to develop a deeper understanding of statistical learning, data mining, and predictive modeling techniques.
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