Risultati di ricerca
8 ott 2024 · James Gareth, Witten Daniela, Hastie Trevor, and Tibshirani Robert. 2013. An introduction to statistical learning: with applications in R. Spinger.
9 ott 2024 · An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years.
8 ott 2024 · Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University.
3 giorni fa · This book provides hands-on modules for many of the most common machine learning methods to include: Generalized low rank models. Clustering algorithms. Autoencoders. Regularized models. Random forests. Gradient boosting machines. Deep neural networks. Stacking / super learners.
9 ott 2024 · This paper also utilizes the concepts of machine learning, especially deep learning, which are drawn from An Introduction to Statistical Learning: With Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2013), and Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016).
5 ott 2024 · In this paper, we seek to direct the attention of the behavioral and mental health-sensing community toward the importance of data imputation in such studies. In this work, we evaluate and benchmark off-the-shelf imputation strategies using the open-source GLOBEM platform and datasets.
1 ott 2024 · Robert Tibshirani, Stanford University. Pre-training and the lasso. Oct 1, 2024, 4:30 pm – 5:30 pm. 101 - Sherrerd Hall. Details. Pre-training is a popular and powerful paradigm in machine learning.
