β Scribed by Vasantha Kalyani David, Sundaramoorthy Rajasekaran (auth.)
No coin nor oath required. For personal study only.
Pattern recognition already figures large in our world, and the possibilities in fields as diverse as climate, culture and history are enormous. This book explores neural networks and functional networks for possible tracks of pattern recognition.
Front Matter....Pages -
Retracted Chapter: Introduction....Pages 1-7
Retracted Chapter: Review of Architectures Relevant to the Investigation....Pages 9-13
Retracted Chapter: Recognition of English and Tamil Alphabets Using Kohonenβs Self-organizing Map....Pages 15-26
Retracted Chapter: Adaptive Resonance Theory Networks....Pages 27-49
Retracted Chapter: Applications of MicroARTMAP....Pages 51-71
Retracted Chapter: Wavelet Transforms and MicroARTMAP....Pages 73-91
Retracted Chapter: Gesture and Signature Recognition Using MicroARTMAP....Pages 93-113
Retracted Chapter: Solving Scheduling Problems with Competitive Hopfield Neural Networks....Pages 115-122
Retracted Chapter: Functional Networks....Pages 123-134
Retracted Chapter: Conclusions and Suggestions for Future Work....Pages 135-136
Erratum to: Pattern Recognition Using Neural and Functional Networks....Pages 137-137
Back Matter....Pages -
Appl.Mathematics/Computational Methods of Engineering; Software Engineering
π SIMILAR VOLUMES
properly designed multi-layer networks can learn complex mappings in high-dimensional spaces without requiring complicated hand-crafted feature extractors.
This book is one of the most up-to-date and cutting-edge texts available on the rapidly growing application area of neural networks. Neural Networks and Pattern Recognition focuses on the use of neural networksin pattern recognition, a very important application area for neural networks technology.
Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and ho
Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and ho