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27 de jun. de 2024 · This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and ...
- Brian D. Ripley, N. L. Hjort
- 1996
27 de jun. de 2024 · Neural networks have arisen from analogies with models of the way that humans might approach pattern recognition tasks, although they have developed a long way from the biological roots. Great claims have been made for these procedures, and although few of these claims have withstood careful scrutiny, neural network methods have had ...
It is an in-depth study of methods for pattern recognition drawn from engineering, statistics, machine learning and neural networks. All the modern branches of the subject are covered, together with case studies of applications.
This chapter addresses topics on the dynamic behavior of neural networks as they oscillate and produce specific timing patterns in their activity. A network of simple processing units is capable of producing prolonged self-sustained oscillations and even chaotic behavior.
1 de jan. de 2001 · Pattern Recognition (PR) is a fast growing field with applications in many diverse areas such as optical character recognition (OCR), computer – aided diagnosis and speech recognition, to name but a few. Download to read the full chapter text.
- Sergios Theodoridis, Konstantinos Koutroumbas
- 2001
A survey of latest approaches for pattern recognition including soft computing based methods like artificial neural network, fuzzy logic, and genetic algorithms and the Hybrid Models is presented and a unified framework of traditional and soft computing is proposed.
The ANNPR 2020 proceedings on artificial neural networks in pattern recognition focus on machine learning approaches, theory, and algorithms, neural networks, computer vision, speech recognition, clustering and classification, machine learning theory, and supervised and unsupervised learning.
