This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include:Deep architecturesRecurrent, recursive, and graph neural networksCellular neural networksBayesian networksApproximation capabilities of neural networksSemi-supervised learningStatistical relational learningKernel methods for structured dataMultiple classifier systemsSelf organisation and modal learningApplications to content-based image retrieval, text mining in large document collections, and bioinformaticsThis book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms. | Handbook on Neural Information Processing by Monica Bianchini, Hardcover | Indigo Chapters