The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.
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The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.
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This chapter presents a broad overview of Radial Basis Function Networks (RBFNs), and facilitates an understanding of their properties by using concepts from approximation theory, catastrophy theory and statistical pattern recognition. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
softmax units, vowel network, consonant network, perimetry data, pose invariance, learned invariance, extrapolating networks, formant speech synthesizer, saturation artefacts, industrial actuator benchmark, sigmoid output units, inherent invariance, prototype layer, formant parameters, candidate neurons, ventricular late potentials, pose angles, extracted feature vectors, using radial basis function networks, programmed ventricular stimulation, actuator fault, confusion classes, variable stepsize, orientation histograms, vowel space Key Phrases - Capitalized Phrases (CAPs): (learn more)
Neural Computation, New York, International Conference, Acc Sensi Speci, Morgan Kaufmann, Neural Information Processing Systems, San Mateo, Complex Systems, Springer Verlag, Pictures of Facial Affect, World Congress, Cambridge University Press, Computer Society Press, Elsevier Science Publishers, Eur Heart, Image Shift Posmmn Image Scaling, New Engl, Period Figure, Pose Pose, Prentice Hall, San Diego New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
This chapter presents a broad overview of Radial Basis Function Networks (RBFNs), and facilitates an understanding of their properties by using concepts from approximation theory, catastrophy theory and statistical pattern recognition. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
softmax units, vowel network, consonant network, perimetry data, pose invariance, learned invariance, extrapolating networks, formant speech synthesizer, saturation artefacts, industrial actuator benchmark, sigmoid output units, inherent invariance, prototype layer, formant parameters, candidate neurons, ventricular late potentials, pose angles, extracted feature vectors, using radial basis function networks, programmed ventricular stimulation, actuator fault, confusion classes, variable stepsize, orientation histograms, vowel space Key Phrases - Capitalized Phrases (CAPs): (learn more)
Neural Computation, New York, International Conference, Acc Sensi Speci, Morgan Kaufmann, Neural Information Processing Systems, San Mateo, Complex Systems, Springer Verlag, Pictures of Facial Affect, World Congress, Cambridge University Press, Computer Society Press, Elsevier Science Publishers, Eur Heart, Image Shift Posmmn Image Scaling, New Engl, Period Figure, Pose Pose, Prentice Hall, San Diego New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 60
books:
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- Advances in Neural Information Processing Systems 12 by Todd K. Leen on page 35, page 101, and page 141
- Neural Networks (Quantitative Applications in the Social Sciences) by Hervé Abdi on page 139, page 141, and page 315
- Neural Networks for Pattern Recognition by Christopher M. Bishop on page 30, page 138, and page 212
- Approximation Theory, Spline Functions and Applications (NATO Science Series C: (closed)) by S.P. Singh on page 33, and page 293
- Numerical Recipes: The Art of Scientific Computing by William H. Press on page 58, and page 141
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