Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This research book presents some of the most recent advances in neural information processing models including both theoretical concepts and practical applications. The contributions include: Advances in neural information processing paradigms Self organising structures Unsupervised and supervised learning of graph domains Neural grammar networks Model complexity in neural network learning Regularization and suboptimal solutions in neural learning Neural networks for the classification of vectors, sequences and graphs Metric learning for prototype-based classification Ensembles of neural networks Fraud detection using machine learning Computational modelling of neural multimodal integration This book is directed to the researchers, graduate students, professors and practitioner interested in recent advances in neural information processing paradigms and applications.

Neural networks (Computer science) --- Neural computers --- Applied Mathematics --- Civil Engineering --- Computer Science --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Neural computers. --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Neural net computers --- Neural network computers --- Neurocomputers --- Computer science. --- Neurosciences. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Appl.Mathematics/Computational Methods of Engineering. --- Engineering --- Engineering analysis --- Mathematical analysis --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural sciences --- Neurological sciences --- Neuroscience --- Medical sciences --- Nervous system --- Informatics --- Science --- Mathematics --- Artificial intelligence --- Natural computation --- Soft computing --- Electronic digital computers --- Artificial Intelligence. --- Mathematical and Computational Engineering.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Mathematical modeling is routinely used in physical and engineering sciences to help understand complex systems and optimize industrial processes. Mathematical modeling differs from Artificial Intelligence because it does not exclusively use the collected data to describe an industrial phenomenon or process, but it is based on fundamental laws of physics or engineering that lead to systems of equations able to represent all the variables that characterize the process. Conversely, Machine Learning methods require a large amount of data to find solutions, remaining detached from the problem that generated them and trying to infer the behavior of the object, material or process to be examined from observed samples. Mathematics allows us to formulate complex models with effectiveness and creativity, describing nature and physics. Together with the potential of Artificial Intelligence and data collection techniques, a new way of dealing with practical problems is possible. The insertion of the equations deriving from the physical world in the data-driven models can in fact greatly enrich the information content of the sampled data, allowing to simulate very complex phenomena, with drastically reduced calculation times. Combined approaches will constitute a breakthrough in cutting-edge applications, providing precise and reliable tools for the prediction of phenomena in biological macro/microsystems, for biotechnological applications and for medical diagnostics, particularly in the field of precision medicine.

Mathematical models.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Adaptive control systems. --- Artificial intelligence. --- Neural networks (Computer science). --- Engineering & Applied Sciences --- Computer Science --- Neural networks (Computer science) --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Engineering. --- Computational intelligence. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Construction --- Industrial arts --- Technology --- Natural computation --- Artificial Intelligence. --- Machine learning.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Medical genetics --- RNA-seq --- algorithm --- software pipeline --- method assessment --- differenial analysis