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27
Automatic Generation of GRBF Networks for Visual Learning
 In Proc. Int. Conf. Computer Vision
, 1995
"... Learning can often be viewed as the problem of mapping from an input space to an output space. Examples of these mappings are used to construct a continuous function that approximates given data and generalizes for intermediate instances. Generalized Radial Basis Function (GRBF) networks are used to ..."
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Cited by 3 (0 self)
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Learning can often be viewed as the problem of mapping from an input space to an output space. Examples of these mappings are used to construct a continuous function that approximates given data and generalizes for intermediate instances. Generalized Radial Basis Function (GRBF) networks are used
Automatic Generation of GRBF Networks Using the Integral Wavelet Transform
"... Learning can often be viewed as the problem of mapping from an input space to an output space. Examples of these mappings are used to construct a continuous function that approximates given data and generalizes for intermediate instances. Generalized Radial Basis Function (GRBF) networks are used to ..."
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Learning can often be viewed as the problem of mapping from an input space to an output space. Examples of these mappings are used to construct a continuous function that approximates given data and generalizes for intermediate instances. Generalized Radial Basis Function (GRBF) networks are used
Recognition of the Sequential Motion (Hand ShapeChange) by the GRBF Network
, 2003
"... this paper, we have applied this network to the recognition of sequential motion and designed the system to capture motions of the hand with the GRBF by using data glove [4] ..."
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this paper, we have applied this network to the recognition of sequential motion and designed the system to capture motions of the hand with the GRBF by using data glove [4]
A Connection between GRBF and MLP
, 1992
"... Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approximation properties, theoretically and experimentally. Are they related ? The main point of this paper is to show that for normalized inputs, multilayer perceptron networks are radial function networks ..."
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Cited by 2 (1 self)
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Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approximation properties, theoretically and experimentally. Are they related ? The main point of this paper is to show that for normalized inputs, multilayer perceptron networks are radial function networks
A Theory of Networks for Approximation and Learning
 Laboratory, Massachusetts Institute of Technology
, 1989
"... Learning an inputoutput mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, t ..."
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Cited by 235 (24 self)
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techniques that leads to a class of threelayer networks that we call Generalized Radial Basis Functions (GRBF), since they are mathematically related to the wellknown Radial Basis Functions, mainly used for strict interpolation tasks. GRBF networks are not only equivalent to generalized splines
Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques
 IEEE Transactions on Neural Networks
, 1997
"... Abstract—This paper proposes a framework for constructing and training radial basis function (RBF) neural networks. The proposed growing radial basis function (GRBF) network begins with a small number of prototypes, which determine the locations of radial basis functions. In the process of training, ..."
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Cited by 58 (3 self)
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Abstract—This paper proposes a framework for constructing and training radial basis function (RBF) neural networks. The proposed growing radial basis function (GRBF) network begins with a small number of prototypes, which determine the locations of radial basis functions. In the process of training
Incremental Learning Method of GRBF with Recalling of Interfered Patterns  Application for Case Based Reasoning Systems
"... This paper proposes a lowcost incremental learning method of Generalized Radial Basis Function (GRBF) for a Case Based Reasoning (CBR) system. A CBR system is one type of reasoning system that uses past cases for solving new problems. To realize the reasoning, the system has to search a past case w ..."
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This paper proposes a lowcost incremental learning method of Generalized Radial Basis Function (GRBF) for a Case Based Reasoning (CBR) system. A CBR system is one type of reasoning system that uses past cases for solving new problems. To realize the reasoning, the system has to search a past case
A Nonlinear MESFET Model for Intermodulation Analysis Using a Generalized Radial Basis Function Network
"... In this paper we use a Generalized Radial Basis Function (GRBF) network to model the intermodulation properties of microwave GaAs MESFET transistors under dynamic operation. The proposed model receives as input the bias voltages of the transistor and provides as output the derivatives of the draint ..."
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Cited by 5 (4 self)
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In this paper we use a Generalized Radial Basis Function (GRBF) network to model the intermodulation properties of microwave GaAs MESFET transistors under dynamic operation. The proposed model receives as input the bias voltages of the transistor and provides as output the derivatives of the drain
Automatic Generation of RBF Networks Using Wavelets
"... Learning can be viewed as mapping from an input space to an output space. Examples of these mappings are used to construct a continuous function that approximates the given data and generalizes for intermediate instances. Radial basis function (RBF) networks are used to formulate this approximating ..."
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Cited by 3 (0 self)
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function. A novel method is introduced that automatically constructs a Generalized radial basis function (GRBF) network for a given mapping and error bound. This network is shown to be the smallest network within the error bound for the given mapping. The integral wavelet transform is used to determine
Improving the Performance of Radial Basis Function Networks by Learning Center Locations
 In
, 1992
"... Three methods for improving the performance of (gaussian) radial basis function (RBF) networks were tested on the NETtalk task. In RBF, a new example is classified by computing its Euclidean distance to a set of centers chosen by unsupervised methods. The application of supervised learning to learn ..."
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Cited by 55 (3 self)
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to learn a nonEuclidean distance metric was found to reduce the error rate of RBF networks, while supervised learning of each center's variance resulted in inferior performance. The best improvement in accuracy was achieved by networks called generalized radial basis function (GRBF) networks. In GRBF
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