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She was elected as a Fellow of the American Statistical Association in 2020. [19] She was named to the 2022 class of Fellows of the Institute of Mathematical Statistics, for "substantial contributions to the field of statistical machine learning, with applications to biology; and for communicating the fundamental ideas in the field to a broad audience".
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [ 1 ] [ 2 ] [ 3 ] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
David S. Stoffer is an American statistician, and Professor Emeritus of Statistics at the University of Pittsburgh. [1] He is the author of several books on time series analysis, Time Series Analysis and Its Applications: With R Examples [2] with R.H. Shumway, Nonlinear Time Series: Theory, Methods, and Applications with R Examples [3] with R. Douc and E. Moulines, and Time Series: A Data ...
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
Decision trees are a popular method for various machine learning tasks. Tree learning is almost "an off-the-shelf procedure for data mining", say Hastie et al., "because it is invariant under scaling and various other transformations of feature values, is robust to inclusion of irrelevant features, and produces inspectable models.
In general, the risk () cannot be computed because the distribution (,) is unknown to the learning algorithm. However, given a sample of iid training data points, we can compute an estimate, called the empirical risk, by computing the average of the loss function over the training set; more formally, computing the expectation with respect to the empirical measure:
Introduction to statistical decision theory. Author: John W. Pratt, Howard Raiffa, and Robert Schlaifer Publication data: preliminary edition, 1965. Cambridge, Mass.: MIT Press, 1995. Description: Extensive exposition of statistical decision theory, statistics, and decision analysis from a Bayesian standpoint. Many examples and problems come ...
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