BookDOI
An introduction to statistical learning
TLDR
An introduction to statistical learning provides an accessible overview of the essential toolset for making sense of the vast and complex data sets that have emerged in science, industry, and other sectors in the past twenty years.Abstract:
Statistics An Intduction to Stistical Lerning with Applications in R An Introduction to Statistical Learning provides an accessible overview of the fi eld of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fi elds ranging from biology to fi nance to marketing to astrophysics in the past twenty years. Th is book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classifi cation, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fi elds, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical soft ware platform. Two of the authors co-wrote Th e Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. Th is book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. Th e text assumes only a previous course in linear regression and no knowledge of matrix algebra.read more
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Proceedings Article
Lifted variable elimination with arbitrary constraints
TL;DR: This work empirically demonstrates that generalized inference methods can be generalized to work with arbitrary constraints, allowing exact inference in cases where until now only approximate inference was feasible.
Journal ArticleDOI
Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal
TL;DR: It was found that the brain activity of patients with depression was related to depression severity, and the presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.
Journal ArticleDOI
Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning.
TL;DR: In this work, an efficient method is developed based on the machine learning (ML) algorithm combined with high-throughput screening that provides an efficient way of searching for novel 2DPV materials, but can be applied to a broad field of functional material exploring.
Journal ArticleDOI
Analytics for Cross-Border E-Commerce: Inventory Risk Management of an Online Fashion Retailer
Ye Shi,Ting Wang,Layth C. Alwan +2 more
Journal ArticleDOI
Intraspecific differentiation: Implications for niche and distribution modelling
TL;DR: This work addresses issues in the case of very small datasets inherent to subclade models and discusses which modelling strategy should be applied based on niche overlap among lineages.