Neural Networks and Statistical Learning
Buch in deiner Nähe kaufen
Beschreibung
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing.
Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include:
• multilayer perceptron;• the Hopfield network;
• associative memory models;
• clustering models and algorithms;
• t he radial basis function network;
• recurrent neural networks;
• nonnegative matrix factorization;
• independent component analysis;
•probabilistic and Bayesian networks; and
• fuzzy sets and logic.
Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing.
Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include:
• multilayer perceptron;
• the Hopfield network;
• associative memory models;• clustering models and algorithms;
• t he radial basis function network;
• recurrent neural networks;
• nonnegative matrix factorization;
• independent component analysis;
•probabilistic and Bayesian networks; and
• fuzzy sets and logic.
Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Extensively updated second edition with new chapters on spar coding, deep learning, big data and cloud computing A comprehensive introduction to neural networks and statistical learning from a practical perspective Includes two appendices with mathematical essentials, as well as benchmarks and resources Collects popular neural models covering the majority of essential neural network applications
Autor*in
Ke-Lin Du
Themen in »Neural Networks and Statistical Learning«
Data Mining, Data Fusion and Ensemble Learning Multilayer Perceptrons Neural Networks Pattern Recognition Statistical and Machine Learning Neural Networks Textbook Statistical Learning Textbook Neural Networking Techniques Spar Coding Deep Learning Big Data Cloud Computing
Stimmen zu »Neural Networks and Statistical Learning«
“Neural Networks and Statistical Learning by Ke-Lin Du and M. N. S. Swamy can be seen as a central reference point for the mathematical understanding and implementation of the core ideas of neuronal networks and statistical learning techniques.” (Jan Pablo Burgard, SIAM Review, Vol. 62 (4), 2020)
()