Open Access
The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Edition
TLDR
In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.Abstract:
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
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. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.read more
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Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco
Dominik Moritz,Chenglong Wang,Greg L. Nelson,Halden Lin,Adam M. Smith,Bill Howe,Jeffrey Heer +6 more
TL;DR: This work proposes modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data, which can take theoretical design knowledge and express it in a concrete, extensible, and testable form.
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Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction
TL;DR: This work describes and provides examples of how artificial intelligence will affect labor, emphasizing differences between when automating prediction leads to automating decisions versus enhancing decision-making by humans.
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Object Categorization, Computer and Human Vision Perspectives
Jie Yu,Dhiraj Joshi +1 more
TL;DR: This PDF file contains the editorial “Object Categorization, Computer and Human Vision Perspectives” for JEI Vol.
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Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets.
Marc-Andre Schulz,B.T. Thomas Yeo,Joshua T. Vogelstein,Janaina Mourao-Miranada,Jakob Nikolas Kather,Jakob Nikolas Kather,Konrad P. Kording,Blake A. Richards,Danilo Bzdok +8 more
TL;DR: This work systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references to benchmark performance scaling with increasingly sophisticated prediction algorithms and with increasing sample size in reference machine-learning and biomedical datasets.
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Tutorial: multivariate classification for vibrational spectroscopy in biological samples.
TL;DR: A tutorial for multivariate classification analysis of vibrational spectroscopy data (Fourier-transform infrared, Raman and near-IR) is presented and guidelines are provided for data preprocessing, data selection, feature extraction, classification and model validation.