نتایج جستجو برای: nurtures basis

تعداد نتایج: 382823  

Journal: :IEEE transactions on neural networks 1999
Ludmila I. Kuncheva James C. Bezdek

We extend the nearest prototype classifier to a generalized nearest prototype classifier (GNPC). The GNPC uses "soft" labeling of the prototypes in the classes, thereby encompassing a variety of classifiers. Based on how the prototypes are found we distinguish between presupervised and postsupervised GNPC designs. We derive the conditions for optimality (relative to the standard Bayes error rat...

Journal: :Remote Sensing 2017
Zhihong Liao Qing Dong Cunjin Xue Jingwu Bi Guangtong Wan

A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the OISST products accordi...

2002
Sukhbinder Kumar Elaine B. Martin Julian Morris

A non-linear version of the multivariate statistical technique of canonical correlation analysis (CCA) is proposed through the integration of a radial basis function (RBF) network. The advantage of the RBF network is that the solution of linear CCA can be used to train the network and hence the training effort is minimal. Also the canonical variables can be extracted simultaneously. It is shown...

1998
J. Tin-Yau Kwok

In this paper, we study the incorporation of the support vector machine (SVM) into the (hierarchical) mixture of experts model to form a support vector mixture. We show that, in both classification and regression problems, the use of a support vector mixture leads to quadratic programming (QP) problems that are very similar to those for a SVM, with no increase in the dimensionality of the QP pr...

Journal: :IEEE Trans. Geoscience and Remote Sensing 1999
Lorenzo Bruzzone Diego Fernández-Prieto

In this paper, a supervised technique for training radial basis function (RBF) neural network classifiers is proposed. Such a technique, unlike traditional ones, considers the class memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The result is twofold: a significant reduction in the overall classifica...

1994
Paul Yee Simon Haykin

Pattern classiication may be viewed as an ill-posed, inverse problem to which the method of regularization be applied. In doing so, a proper theoretical framework is provided for the application of radial basis function (RBF) networks to pattern classiication, with strong links to the classical kernel regression estimator (KRE)-based classiiers that estimate the underlying posterior class densi...

1985
Jonathan .TENNYSON

A method of evaluating matrix elements between polynomial basis functions is proposed involving the explicit expansion of the operator in terms of the polynomial. The method is shown to have several advantages over the direct evaluation of these matrix elements by Gaussian quadrature, including savings of up to a factor of 6N for an N-dimensional integral. Application to the calculation of poly...

2001
Liangyin Yu Charles R. Dyer

2D curve representations usually take algebraic forms in ways not related to visual perception. This poses great difficulties in connecting curve representation with object recognition where information computed from raw images must be manipulated in a perceptually meaningful way and compared to the representation. In this paper we show that 2D curves can be represented compactly by imposing sh...

1994
Bernd Fritzke

We present a new incremental radial basis function network suitable for classiication and regression problems. Center positions are continuously updated through soft competitive learning. The width of the radial basis functions is derived from the distance to topological neighbors. During the training the observed error is accumulated locally and used to determine where to insert the next unit....

Journal: :Pattern Recognition Letters 1997
Young-Sup Hwang Sung Yang Bang

Among the neural network models RBF(Radial Basis Function) network seems to be quite effective for a pattern recognition task such as handwritten numeral recognition since it is extremely flexible to accommodate various and minute variations in data. Recently we obtained a good recognition rate for handwritten numerals by using an RBF network. In this paper we show how to design an RBF network ...

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