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Engineering - Computational Intelligence and Complexity | Neural Networks and Statistical Learning

Neural Networks and Statistical Learning

Du, Ke-Lin, Swamy, M. N. S.

2014, XXVII, 824 p. 166 illus., 68 illus. in color.

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  • Provides a comprehensive introduction to neural networks and statistical learning ensuring a broad yet in-depth coverage of the techniques focusing on  the prominent accomplishments in practical aspects
  • Divided into twenty-five chapters and two appendices including mathematical preliminaries, and benchmarks and resources explaining the start-of-art descriptions of all important recent research results on the respective topic to provide a single point of reference for future research
  • Collects popular neural models covering the majority of neural network application essential to all students and researchers in this field

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content.

Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included.

Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Content Level » Graduate

Keywords » Data Mining, Data Fusion and Ensemble Learning - Multilayer Perceptrons - Neural Networks - Pattern Recognition - Statistical and Machine Learning

Related subjects » Computational Intelligence and Complexity - Database Management & Information Retrieval - Image Processing

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