Category Archives: Machine Learning

Introduction to Machine Learning, Second Edition (Adaptive Computation and Machine Learning)

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, … Continue reading

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Kernel Adaptive Filtering: A Comprehensive Introduction (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)

Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book … Continue reading

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Machine Learning for Audio, Image and Video Analysis: Theory and Applications

Machine Learning involves several scientific domains including mathematics, computer science, statistics and biology, and is an approach that enables computers to automatically learn from data. Focusing on complex media and how to convert raw data into useful information, this book … Continue reading

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Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF’s various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF/NTF and their extensions are increasingly used … Continue reading

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Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics)

Sure to be influential, Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here … Continue reading

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Computer Recognition Systems 3 (Advances in Intelligent and Soft Computing)

The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 69 carefully selected articles contributed by experts of pattern recognition. … Continue reading

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Solar Image Analysis and Visualization

This title collects refereed papers submitted to the Third Solar Image Processing Workshop, (6-8 September, 2006, Trinity College Dublin, Ireland). The workshop brought together researchers in image processing and solar physics to focus on the challenges presented by new ground-based … Continue reading

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Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design (Premier Reference Source)

Today, fuzzy methods are of common use as they provide tools to handle data sets in a relevant, robust, and interpretable way, making it possible to cope with both imprecision and uncertainties. Scalable Fuzzy Algorithms for Data Management and Analysis: … Continue reading

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Advances in Machine Learning II: Dedicated to the memory of Professor Ryszard S. Michalski (Studies in Computational Intelligence)

This is the second volume of a large two-volume editorial project we wish to dedicate to the memory of the late Professor Ryszard S. Michalski who passed away in 2007. He was one of the fathers of machine learning, an … Continue reading

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Innovations in Machine Learning: Theory and Applications

Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three … Continue reading

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