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The book presents a coherent understanding of computational intelligence from the perspective of what is known as "intelligent computing" with high-dimensional parameters. It critically discusses the central issue of high-dimensional neurocomputing, such as quantitative representation of signals, extending the dimensionality of neuron, supervised and unsupervised learning and design of higher order neurons. The strong point of the book is its clarity and ability of the underlying theory to unify our understanding of high-dimensional computing where conventional methods fail. The plenty of application oriented problems are presented for evaluating, monitoring and maintaining the stability of adaptive learning machine. Author has taken care to cover the breadth and depth of the subject, both in the qualitative as well as quantitative way. The book is intended to enlighten the scientific community, ranging from advanced undergraduates to engineers, scientists and seasoned researchers in computational intelligence.
List of contents
Neuro-Computing with High Dimensional Parameters.- Neuro-Computing in Complex Domain.- Higher Order Computational Model of Novel Neurons.- Neuro-Computing in Space.- High Dimensional Mapping.- Machine Recognition for Biometric Application in Complex Domain.
About the author
Dr. Bipin Kumar Tripathi completed his PhD in Computational Intelligence from IIT Kanpur, India and M. Tech in Computer Science and Engineering from IIT Delhi, India. Dr. Tripathi is currently serving as an Associate Professor in Department of Computer Science and Engineering of HBTI Kanpur, India. He is also leading the Nature-inspired Computational Intelligence Research Group (NCIRG) at HBTI. His areas of research include high-dimensional neurocomputing, computational neuroscience, intelligent system design, machine learning and computer vision focused on biometrics and 3D Imaging. He has published several research papers in these areas in many peer reviewed journals including IEEE Transaction/Elsevier/Springer and other international conferences. He has also contributed book chapters in different international publications and patent in his area. He is continuously serving as PC for many international conferences and as a reviewer of several international journals.
Summary
The book presents a coherent understanding of computational intelligence from the perspective of what is known as "intelligent computing" with high-dimensional parameters. It critically discusses the central issue of high-dimensional neurocomputing, such as quantitative representation of signals, extending the dimensionality of neuron, supervised and unsupervised learning and design of higher order neurons. The strong point of the book is its clarity and ability of the underlying theory to unify our understanding of high-dimensional computing where conventional methods fail. The plenty of application oriented problems are presented for evaluating, monitoring and maintaining the stability of adaptive learning machine. Author has taken care to cover the breadth and depth of the subject, both in the qualitative as well as quantitative way. The book is intended to enlighten the scientific community, ranging from advanced undergraduates to engineers, scientists and seasoned researchers in computational intelligence.
