So I complain in this review instead. September 2020, Rezension aus Deutschland vom 26. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. November 2018, Rezension aus Deutschland vom 3. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. November 2016. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. Momentanes Problem beim Laden dieses Menüs. Februar 2009 von Trevor Hastie (Autor), Robert Tibshirani (Autor), Jerome Friedman (Autor) 2000. Sind Sie der Meinung, dass dieser Artikel Urheberrechte verletzt? Juli 2019. Geben Sie es weiter, tauschen Sie es ein, © 1998-2020, Amazon.com, Inc. oder Tochtergesellschaften, Entdecken Sie Robert Tibshirani bei Amazon, Wahrscheinlichkeit & Statistik (englischsprachig), Künstliche Intelligenz (englischsprachig), Übersetzen Sie alle Bewertungen auf Deutsch, Lieferung verfolgen oder Bestellung anzeigen, Recycling (einschließlich Entsorgung von Elektro- & Elektronikaltgeräten). As many other reviews have covered, this is an important text book, and covers a wide array of topics in suitable detail. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. Enthält dieses Buch Qualitäts- oder Formatierungsprobleme? I have subtracted two stars due to the atrocious print quality, some of the references cannot be read as they are so blurry, the spine is coming apart, and the pages are bound unevenly. Geben Sie Ihre Mobiltelefonnummer ein, um die kostenfreie App zu beziehen. The authors give precise, practical explanations of what methods are available, and … It is a valuable resource for statisticians and anyone interested in data mining in science or industry. [...], Web Development From Scratch: Learn By Doing For Complete Beginners. 213.215.83.1, Trevor Hastie, Robert Tibshirani, Jerome Friedman, https://doi.org/10.1007/978-0-387-84858-7, Additive Models, Trees, and Related Methods, Support Vector Machines and Flexible Elements of statistic learning is one of the most important textbooks on algorithm analysis in the field of machine learning. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. Juli 2014. 3 Personen fanden diese Informationen hilfreich, Rezension aus Deutschland vom 5. Many examples are given, with a liberal use of color graphics. I ordered the book for delivery in advance of a trip and only now got to see it. © 2020 Springer Nature Switzerland AG. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. Having completed the Coursera Stanford Machine Learning course I wanted to know more and this came up at the top recommended book in Amazon for ML. Continue reading HTML and CSS for Beginners – Build a Website & Launch ONLINE at FreeCourses.Info. There are a lot of typos in this text especially in the equations, e.g., "(3" where there should be the greek letter beta. Elements of Statistical Learning: data mining, inference, and prediction. 5 Personen fanden diese Informationen hilfreich, Rezension aus dem Vereinigten Königreich vom 29. While the approach is statistical, the emphasis is on concepts rather than mathematics. 4 Personen fanden diese Informationen hilfreich, Rezension aus Deutschland vom 16. Februar 2009, Beliebte Taschenbuch-Empfehlungen des Monats, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition…. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. Many examples are given, with a liberal use of color graphics. 5 Personen fanden diese Informationen hilfreich, Rezension aus dem Vereinigten Königreich vom 29. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. Geben Sie einen Promotionscode oder einen Geschenkgutschein ein. I downloaded the free PDF but it's huge and I find it impossible to read a PDF on a screen so I forked out for the hardback paper copy. Continue reading Web Design for Web Developers: Build Beautiful Websites! Would have been perfect if not for this production flaw (that was never noted). Mai 2020. 14 Personen fanden diese Informationen hilfreich, Rezension aus dem Vereinigten Königreich vom 24. Springer is part of, Please be advised Covid-19 shipping restrictions apply. The Elements of Statistical Learning. Prime-Mitglieder genießen Zugang zu schnellem und kostenlosem Versand, tausenden Filmen und Serienepisoden mit Prime Video und vielen weiteren exklusiven Vorteilen. Wählen Sie ein Land/eine Region für Ihren Einkauf. Please review prior to ordering, The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book, Includes more than 200 pages of four-color graphics, ebooks can be used on all reading devices, Institutional customers should get in touch with their account manager, The final prices may differ from the prices shown due to specifics of VAT rules. Continue reading Web Development By Doing: HTML / CSS From Scratch at FreeCourses.Info. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. [...], Learn how Javascript works, some basic API's and finally create a mini project. Insbesondere Format und Typographie sind schöner Latex Satz ohne zu viel Eingriffen von Seiten des Verlages und von daher ist das Buch eine Augenweide. Es wird kein Kindle Gerät benötigt. While the approach is statistical, the emphasis is on concepts rather than mathematics. Um aus diesem Karussell zu navigieren, benutzen Sie bitte Ihre Überschrift-Tastenkombination, um zur nächsten oder vorherigen Überschrift zu navigieren. Sie hören eine Hörprobe des Audible Hörbuch-Downloads. ...you'll find more products in the shopping cart. Part of Springer Nature. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) (English Edition). Nachdem Sie Produktseiten oder Suchergebnisse angesehen haben, finden Sie hier eine einfache Möglichkeit, diese Seiten wiederzufinden. 3), "This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters … were included. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. pdfs / The Elements of Statistical Learning - Data Mining, Inference and Prediction - 2nd Edition (ESLII_print4).pdf Go to file Go to file T; Go to line L; Copy path tpn Fix permissions. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. The authors of this book, Trevor Hastie, Robert Tibshirani and Jerome Friedman, are pioneers in the area and have done really b... ( 展开 ) 2 0回应. November 2016. – ggf. During the past decade there has been an explosion in computation and information technology. Also, the figures are very well made. ), Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series), An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics).