Kurum Yazarı "Barkana, Atalay" Makale Koleksiyonu İçin Listeleme
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Application of Linear Regression Classification to low-dimensional datasets
Koç, Mehmet; Barkana, Atalay (Elsevier Science BV, 2014)The Traditional Linear Regression Classification (LRC) method fails when the number of data in the training set is greater than their dimensions. In this work, we proposed a new implementation of LRC to overcome this problem ... -
Covariance analysis of voltage waveform signature for power-quality event classification
Gerek, Ömer Nezih; Ece, Doğan Gökhan; Barkana, Atalay (IEEE-Inst Electrical Electronics Engineers Inc, 2006)In this paper, covariance behavior of several features (signature identifiers) that are determined from the voltage waveform within a time window for power-quality (PQ) event detection and classification is analyzed. A ... -
Discriminative common vector approach based feature selection in face recognition
Koç, Mehmet; Barkana, Atalay (Pergamon-Elsevier Science LTD, 2014)A novel feature selection algorithm is proposed, which is related to the Discriminative Common Vector Approach (DCVA) utilized as a means to reduce the computational complexity of the facial recognition problem. The ... -
A fast method for the implementation of common vector approach
Koç, Mehmet; Barkana, Atalay; Gerek, Ömer Nezih (Elsevier Science Inc, 2010)In this paper a novel computation method is proposed to perform the common vector approach (CVA) faster than its conventional implementation in pattern recognition. While conventional CVA calculations perform the classification ... -
Kernel common vector method: A Novel nonlinear subspace classifier for pattern recognition
Çevikalp, Hakan; Nearntu, Marian; Barkana, Atalay (IEEE-Inst Electrical Electronics Engineers Inc, 2007)The common vector (CV) method is d linear subspace classifier method which allows one to discriminate between classes of data sets, such as those arising in image and word recognition. This method utilizes subspaces that ... -
A new solution to one sample problem in face recognition using FLDA
Koç, Mehmet; Barkana, Atalay (Elsevier Science Inc, 2011)Fisher linear discriminant analysis (FLDA) is a very popular method in face recognition. But FLDA fails when one image per person is available. This is due to the fact that the within-class scatter matrices cannot be ... -
A Novel Implementation Algorithm For Calculation of
Koç, Mehmet; Barkana, Atalay (2016)Common vector approach (CVA), discriminative common vector approach (DCVA), and linear regression classification (LRC) are subspace methods used in pattern recognition. Up to now, there were two well-known algorithms to ... -
On the realization of common matrix classifier using covariance tensors
Ergin, Semih; Gerek, Ömer Nezih; Gülmezoğlu, M. Bilginer; Barkana, Atalay (Academic Press Inc Elsevier Science, 2015)Due to the growing interest in image classifiers, the concept of native two dimensional (2-D) classifiers continues to attract researchers in the field of pattern recognition. In most cases, the 2-D extension of a regular ... -
Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training
Cetisli, Bayram; Barkana, Atalay (Springer, 2010)The aim of this study is to speed up the scaled conjugate gradient (SCG) algorithm by shortening the training time per iteration. The SCG algorithm, which is a supervised learning algorithm for network-based methods, is ... -
Two-dimensional subspace classifiers for face recognition
Çevikalp, Hakan; Yavuz, Hasan Serhan; Cay, Mehmet Atif; Barkana, Atalay (Elsevier Science BV, 2009)The subspace classifiers are pattern classification methods where linear subspaces are used to represent classes. In order to use the classical subspace classifiers for face recognition tasks, two-dimensional (2D) image ... -
Use of center of gravity with the common vector approach in isolated word recognition
Gülmezoğlu, M. Bilginer; Edizkan, Rıfat; Ergin, Semih; Barkana, Atalay (Pergamon-Elsevier Science LTD, 2011)In this paper, the subspace based classifier, common vector approach (CVA), with the center of gravity (COG) method is used for isolated word recognition. Since the CVA classifier is sensitive to shifts through the time ...