Results 1 - 10 of 27

Automatic Generation of GRBF Networks for Visual Learning

by Shayan Mukherjee, Shree K. Nayar - 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 ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
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

by Shayan Mukherjee, Shree K. Nayar
"... 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 Shape-Change) by the GRBF Network

by Miwako Hirakawa, Masahiro Okamoto , 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

by M. Maruyama, Minoru Maruyama, Federico Girosi, Tomaso Poggio , 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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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

by Tomaso Poggio, Federico Girosi - Laboratory, Massachusetts Institute of Technology , 1989
"... Learning an input-output 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 multi-dimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, t ..."
Abstract - Cited by 235 (24 self) - Add to MetaCart
techniques that leads to a class of three-layer networks that we call Generalized Radial Basis Functions (GRBF), since they are mathematically related to the well-known 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

by Nicolaos B. Karayiannis, Glenn Weiqun Mi - 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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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

by Koichiro Yamauchi, Nobuhiko Yamaguchi, Naohiro Ishii
"... This paper proposes a low-cost 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 low-cost 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

by Ignacio Santamaría, Marcelino Lázaro, Carlos J. Pantaleón, Jose A. García, Antonio Tazón, Angel Mediavilla
"... 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-t ..."
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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

by Shayan Mukherjee, Shree K. Nayar
"... 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 ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
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

by Dietrich Wettschereck, Dietrich Wettschereck, Thomas Dietterich, Thomas Dietterich - 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 ..."
Abstract - Cited by 55 (3 self) - Add to MetaCart
to learn a non-Euclidean 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
Results 1 - 10 of 27