This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.In Chapter 9, by Andrea Passerini, the basic principles underlying kernel machines are revised, together with some of the ... Algorithms, dataset descriptions, pseudocode and Matlab code are included. ... In content based image retrieval (CBIR), relevance feedback is an interactive process, that builds a bridge betweenanbsp;...
|Title||:||Handbook on Neural Information Processing|
|Author||:||Monica Bianchini, Marco Maggini, Lakhmi C. Jain|
|Publisher||:||Springer Science & Business Media - 2013-04-12|