Radial-basis-function networks: learning and applications

We present different training algorithms for radial basis function (RBF) networks. The behaviour of RBF classifiers in three different pattern recognition applications is presented: the classification of 3-D visual objects, high-resolution electrocardiograms and handwritten digits.

[1]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[2]  S. Edelman,et al.  Orientation dependence in the recognition of familiar and novel views of three-dimensional objects , 1992, Vision Research.

[3]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[4]  Rodney A. Brooks,et al.  Model-Based Three-Dimensional Interpretations of Two-Dimensional Images , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[6]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[7]  W. Light Some Aspects of Radial Basis Function Approximation , 1992 .

[8]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[9]  M. Hoher,et al.  Similarities of LVQ and RBF learning-a survey of learning rules and the application to the classification of signals from high-resolution electrocardiography , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[10]  M. V. Rossum,et al.  In Neural Computation , 2022 .

[11]  Bernt Schiele,et al.  Probabilistic object recognition using multidimensional receptive field histograms , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[12]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[13]  James J. Little,et al.  Seeing in Parallel: the Vision Machine , 1988 .

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[16]  H H Bülthoff,et al.  How are three-dimensional objects represented in the brain? , 1994, Cerebral cortex.

[17]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..

[18]  Jooyoung Park,et al.  Approximation and Radial-Basis-Function Networks , 1993, Neural Computation.

[19]  Günther Palm,et al.  An Algorithm for Adaptive Clustering and Visualisation of Highdimensional Data Sets , 2000, Computational Intelligence in Data Mining.

[20]  David L. Sheinberg,et al.  Visual object recognition. , 1996, Annual review of neuroscience.

[21]  John E. Moody,et al.  Fast adaptive k-means clustering: some empirical results , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[22]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[23]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[24]  Bartlett W. Mel SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.

[25]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[26]  M. Kubat,et al.  Decision trees can initialize radial-basis function networks , 1998, IEEE Trans. Neural Networks.

[27]  S Edelman,et al.  A model of visual recognition and categorization. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[28]  C. Micchelli Interpolation of scattered data: Distance matrices and conditionally positive definite functions , 1986 .