Learning radial basis neural networks in a lazy way: A comparative study

Valls, José María; Galván, Inés María; Isasi, Pedro

doi:10.1016/j.neucom.2007.10.030

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…k-NN learning is one of the lazy learning (i.e., IBL) techniques that defer the decision of the parameters of a model until a query point is provided newly. When a query instance is given, a set of similar patterns is determined among the already obtained training patterns set and is used to estimate the output of the new instance [17]. There are two well-known advantages of lazy learning: effective usage of local information available in the input space and short training time [1].…”

Section: Locally Weighted Regression With K-nn Approachmentioning

confidence: 99%

Lazy Learning for Nonparametric Locally Weighted Regression

Roh¹,

Kim²,

Ahn³

2020

IJFIS

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In this study, a newly designed local model called locally weighted regression model is proposed for the regression problem. This model predicts the output for a newly submitted data point. In general, the local regression model focuses on an area of the input space specified by a certain kernel function (Gaussian function, in particular). The local area is defined as a region enclosed by a neighborhood of the given query point. The weights assigned to the local area are determined by the related entries of the partition matrix originating from the fuzzy C-means method. The local regression model related to the local area is constructed using a weighted estimation technique. The model exploits the concept of the nearest neighbor, and constructs the weighted least square estimation once a new query is provided given. We validate the modeling ability of the overall model based on several numeric experiments.

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Section: Locally Weighted Regression With K-nn Approachmentioning

confidence: 99%

Lazy Learning for Nonparametric Locally Weighted Regression

Roh¹,

Kim²,

Ahn³

2020

IJFIS

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“…Such a model should, to perform well, cover the entire input space focusing to achieve greater accuracy in regions of higher probability. Typical examples of lazy learning and eager learning are, respectively, the kNN [6] and RBFN [9] algorithms.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison between Edited kNN and MQ-RBFN for Regression and Classification Tasks

Maia

Laboreiro²,

Chaves³

et al. 2016

Anais Do 11. Congresso Brasileiro De Inteligência Computacional

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Abstract-Supervised learning techniques can be roughly grouped into lazy learning or eager learning. Lazy learning and eager learning have very different properties and are suitable for different applications. In this paper we evaluate properties of the two types of learning using a representative distance based algorithm for each class, namely, kNN (k-nearest neighbors) and RBFN (Radial Basis Function Network). In addition, an edition algorithm (SPAM -Supervised Partitioning Around Medoids) is used to reduce the labeled dataset. Our experiments for classification and regression tasks, using 12 public datasets show that prototype selection algorithms typically used with kNN are good alternatives for selection of centers of RBFN when to optimize the number of centers is not the relevant criterion. The experiments also show that the RBFN generally perform better than Edited kNN.

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“…In [29], we can find a time series analysis using nonlinear dynamic systems theory and models based on multilayer feed-forward networks. These models are applied to data measures of the tide level in the Venice lagoon over the years 1980-1994. In recent works [8,26], learning methods are used to automatically select the most appropriate patterns for training, depending on the example to predict. This training method uses a lazy learning strategy that builds local approximations centered in the new patterns.…”

Section: Introductionmentioning

confidence: 99%

Time series prediction evolving Voronoi regions

2009

Self Cite

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Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.

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Learning radial basis neural networks in a lazy way: A comparative study

Cited by 3 publications

References 20 publications

Lazy Learning for Nonparametric Locally Weighted Regression

Lazy Learning for Nonparametric Locally Weighted Regression

Performance Comparison between Edited kNN and MQ-RBFN for Regression and Classification Tasks

Time series prediction evolving Voronoi regions

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