Boosted Pre-loaded Mixture of Experts for low-resolution face recognition

Ebrahimpour, Reza; Sadeghnejad, Naser; Masoudnia, Saeed; Arani, Seyed Ali Asghar Abbaszadeh

doi:10.3233/his-2012-0153

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…Successful applications of ensembles have been reported in various fields, for instance in the context of spam detection [29], handwritten digit recognition [4], face recognition [13], signature verification [7], image labelling [32], time series classifier [12] and more. In [12] a multiple classifier approach is proposed to improve the accuracy of time series classifiers.…”

Section: Introductionmentioning

confidence: 99%

An automatic method for construction of ensembles to time series prediction

Lima

Ludermir

2013

HIS

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We present here a work that applies an automatic construction of ensembles based on the Clustering and Selection (CS) algorithm for time series forecasting. The automatic method, called CSELM, initially finds an optimum number of clusters for training data set and subsequently designates an Extreme Learning Machine (ELM) for each cluster found. For model evaluation, the testing data set are submitted to clustering technique and the nearest cluster to data input will give a supervised response through its associated ELM. Self-organizing maps were used in the clustering phase. Adaptive differential evolution was used to optimize the parameters and performance of the different techniques used in the clustering and prediction phases. The results obtained with the CSELM method are compared with results obtained by other methods in the literature. Five well-known time series were used to validate CSELM.

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