Pattern recognition and neural networks

By B. D. Ripley, Nils Lid Hjort. 1996

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Citations in the corpus

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. (2019, neutral): “14) of atoms i and j, and χ(rij) is given by χ(rij) = φ(2rij) 1 √ r2 ij + 1 + (1− φ(2rij)) 1 rij (13)”
An extensive experimental survey of regression methods (2019, background): “A 1152 committee of 5 (argument repeats) multi-layer perceptron neural net1153 works of the same size trained using different random seeds, being av1154 eraged to give an output [80].”
Statistical fraud detection: A review (2002, background): “For example, Robinson and Tawn (1995), Smith (1997), and Barao and Tawn (1999) examined the results of running events to see if some exceptional times were out of line with what might be expected.”
Text Categorization Based on Regularized Linear Classification Methods (2001, methodology): “Ripley 1996, chapter 3 ). Similar to linear discriminant analysis, an approach widely used in statistics (usually for regression rather than classification) is the least square fit algorithm.”
Dynamic Trees for Learning and Design (2009, methodology): “ing ^c(x) over the input space provides a predictive classiﬁcation surface. We ﬁrst apply our methodology to the Cushing’s data, again available in the MASS library for R, used throughout the book by Ripley (1996) to illustrate various classiﬁcation algorithms. This simple data set has two inputs – ”
Nonparametric Model Calibration Estimation in Survey Sampling (2005, unknown): “For a detailed review of the theoretical properties of feedforward neural networks see Ripley (1996). In order to estimate the regression function (8), we follow the approach of Wu and Sitter (2001).”
Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables (1999, background): “Several recent textbooks describe the mechanics of ANNs (Hertz et al., 1991; Haykin, 1994; Masters, 1994; Bishop, 1995; Ripley, 1996).”
A Bias Correction Algorithm for the Gini Variable Importance Measure in Classification Trees (2008, background): “” Strobl et al. (2007b) reinterpreted and systematized previous results about this measure and identified three fundamental sources of bias: (a) the bias of the Gini estimator, (b) the variance of the Gini estimator, and (c) the effects of multiple comparisons.”
Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings (2009, methodology): “Cross-validation procedures have been used in a number of classification evaluations, particularly for limited datasets [45], [46].”
Random Forests and Adaptive Nearest Neighbors (2006, background): “See Fix and Hodges (1951), Dasarathy (1991), Ripley (1996), and Hastie, Tibshirani, and Friedman (2001) for summaries of the extensive literature on the topic.”