Normalized Gaussian Radial Basis Function networks

Bugmann, Guido

doi:10.1016/s0925-2312(98)00027-7

Cited by 117 publications

(65 citation statements)

References 6 publications

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“…Normalized Gaussian radial basis function network (Bugmann 1998) uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. SMO SMO (Keerthi et al 2001) is an algorithm of support vector machine classifier that globally transforms nominal attributes into binary ones and multiclass problems are solved using pairwise classification.…”

Section: Normalized Gaussian Radial Basis Function Network (Rbf)mentioning

confidence: 99%

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil

Xie

et al. 2015

Environ Sci Pollut Res

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Prediction of antibiotic pollution and its consequences is difficult, due to the uncertainties and complexities associated with multiple related factors. This article employed domain knowledge and spatial data to construct a Bayesian network (BN) model to assess fluoroquinolone antibiotic (FQs) pollution in the soil of an intensive vegetable cultivation area. The results show: (1) The relationships between FQs pollution and contributory factors: Three factors (cultivation methods, crop rotations, and chicken manure types) were consistently identified as predictors in the topological structures of three FQs, indicating their importance in FQs pollution; deduced with domain knowledge, the cultivation methods are determined by the crop rotations, which require different nutrients (derived from the manure) according to different plant biomass. (2) The performance of BN model: The integrative robust Bayesian network model achieved the highest detection probability (pd) of high-risk and receiver operating characteristic (ROC) area, since it incorporates domain knowledge and model uncertainty. Our encouraging findings have implications for the use of BN as a robust approach to assessment of FQs pollution and for informing decisions on appropriate remedial measures.

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Section: Normalized Gaussian Radial Basis Function Network (Rbf)mentioning

confidence: 99%

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil

Xie

et al. 2015

Environ Sci Pollut Res

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“…In contrast, in standard RBF nets, a significant output requires a hidden node with its centre close to the input vector, or using hidden nodes with wider receptive fields (larger σ ). It was shown in Bugmann (1998) [5] that for best interpolation, standard RBF nets require values of σ close to one half of the largest distance between nearest neighbours (which is here 0.46), reflecting the need to ensure large enough output values over the largest empty space between training data. Performances are better for larger receptive field sizes.…”

Section: The Plateau-valley Classification Problemmentioning

confidence: 99%

“…One of the key features of NRBF nets is their excellent generalization, a property that can be exploited to reduce the number of hidden nodes in classification tasks. A new learning rule was proposed to that effect by Bugmann (1998) [5] and is summarized in section 3.…”

Section: Introductionmentioning

confidence: 99%

Classification Using Networks of Normalized Radial Basis Functions

Bugmann

1999

International Conference on Advances in Pattern Recognition

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Normalized Radial Basis Function Networks (NRBF) were invented at the same time as standard RBF nets, in 1989, but went unnoticed until recently, when it was found that they constitute a very interesting tool, especially for pattern classification. NRBF classifiers behave as Nearest Neighbour classifiers and have a functionality similar to Fuzzy Inference Systems but without the Curse of Dimensionality problem. NRBF nets are easy to use, with performances largely insensitive to model parameters and with a very fast training. In this paper the functionality of NRBF nets is compared with those of standard RBF nets. The applications to classification will be illustrated with benchmark problems. For the IRIS classification problem, NRBF match the performances of the best published techniques.

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“…NGnets differ from RBF networks in the normalization of the Gaussian activation function. The normalization switches the traditional roles of weights and activities in the hidden layer, and NGnets therefore exhibit better generalization properties [1].…”

Section: Introductionmentioning

confidence: 99%

An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting

Backhus

Takigawa

Imai

et al. 2017

IEICE Trans. Fundamentals

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SUMMARY In this paper, we introduce a self-constructive Normalized Gaussian Network (NGnet) for online learning tasks. In online tasks, data samples are received sequentially, and domain knowledge is often limited. Then, we need to employ learning methods to the NGnet that possess robust performance and dynamically select an accurate model size. We revise a previously proposed localized forgetting approach for the NGnet and adapt some unit manipulation mechanisms to it for dynamic model selection. The mechanisms are improved for more robustness in negative interference prone environments, and a new merge manipulation is considered to deal with model redundancies. The effectiveness of the proposed method is compared with the previous localized forgetting approach and an established learning method for the NGnet. Several experiments are conducted for a function approximation and chaotic time series forecasting task. The proposed approach possesses robust and favorable performance in different learning situations over all testbeds.

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Normalized Gaussian Radial Basis Function networks

Cited by 117 publications

References 6 publications

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil

Classification Using Networks of Normalized Radial Basis Functions

An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting

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