The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields. Izenman This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data.
#Classical And Modern Regression With Applications Pdf Download series
490: a The Duxbury advanced series in statistics and decision sciences: 650: 7: a Regression analysis. Myers 1990 Modern Multivariate Statistical Techniques-Alan J. a Classical and modern regression with applications / c Raymond H. Ignoring measurement error in this application yields to. Myers 1986 Classical and Modern Regression with Applications-Raymond H. classical measurement errors, and we do not need supplementary. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. Classical and Modern Regression with Applications-Raymond H. It also investigates the pros and cons of classical training algorithms.
The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. This book provides an in-depth analysis of the current evolutionary machine learning techniques.
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