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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Journal ArticleDOI
Occupants’ interactions with windows in 8 residential apartments in Beijing and Nanjing, China
Shanshan Shi,Bin Zhao +1 more
TL;DR: Wang et al. as discussed by the authors conducted a field study in 8 naturally ventilated residential apartments in Beijing and Nanjing, China and established stochastic models of occupants' interactions with windows via univariate and multivariate linear logistic regression for both cities.
Journal ArticleDOI
Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation
TL;DR: Using deeply recurrent neural networks to account for temporal dependence in electroencephalograph (EEG)-based workload estimation is shown to considerably improve day-to-day feature stationarity resulting in significantly higher accuracy than classifiers which do not consider the temporal dependence encoded within the EEG time-series signal.
Journal ArticleDOI
Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland
TL;DR: The study has demonstrated that UAV acquired image data can be used in biomass estimation in miombo woodlands using automatically generated DTMs.
Journal ArticleDOI
Statistically reinforced machine learning for nonlinear patterns and variable interactions
Masahiro Ryo,Matthias C. Rillig +1 more
TL;DR: The results show the potential of statistically reinforced machine learning algorithms to detect nonlinear relationships and higher-order interactions and a hypothesis using parametric statistics after identifying patterns using statistically reinforcedMachine learning techniques.
Journal ArticleDOI
Moisture insensitive prediction of soil properties from VNIR reflectance spectra based on external parameter orthogonalization
TL;DR: In this paper, external parameter orthogonalization (EPO) has been proposed as a useful method that links dry ground VNIR models to field moist scans to predict soil properties.