• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.560-567
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• 1998

This paper concerned about a imputed color printed material image in camera to decrease noise and distortion by processing median filtering with input image to identical condition. Also this paper proposed the way of compares a normal printed material with an abnormal printed material color tone with trained a learning of the error back-propagation to block classification by extracting five place from identical block(3${\times}$3) of color printed material R, G, B value. As a representative algorithm of multi-layer perceptron the error Back-propagation technique used to solve complex problems. However, the Error Back-propagation is algorithm which basically used a gradient descent method which can be converged to local minimum and the Back Propagation train include problems, and that may converge in a local minimum rather than get a global minimum. The network structure appropriate for a given problem. In this paper, a good result is obtained by improve initial condition and adjust th number of hidden layer to solve the problem of real time process, learning and train.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.170-178
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• 1994

The nearest neighbor classification rule is widely used because it is not only simple but the error rate is asymptotically less than twice Bayes theoretical minimum error. But the method basically use the whole training patterns as the reference vectors. so that both storage and classification time increase as the number of training patterns increases. LVQ(Learning Vector Quantization) resolved this problem by training the reference vectors instead of just storing the whole training patterns. But it is a heuristic algorithm which has no theoretic background there is no terminating condition and it requires a lot of iterations to get to meaningful result. This paper is to propose a new training method of the reference vectors. which minimize the given error function. The nearest neighbor network,the network version of the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule and the reference vectors are represented by the weights between the nodes. The network is trained to minimize the error function with respect to the weights by the steepest descent method. The learning algorithm is derived and it is shown that the proposed method can adjust more reference vectors than LVQ in each iteration. Experiment showed that the proposed method requires less iterations and the error rate is smaller than that of LVQ2.

• Journal of Astronomy and Space Sciences
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• v.20 no.2
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• pp.101-108
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• 2003

This study reports one approach for the classification of magnetic storms into recurrent patterns. A storm event is defined as a local minimum of Dst index. The analysis of Dst index for the period of year 1957 through year 2000 has demonstrated that a large portion of the storm events can be classified into a set of recurrent patterns. In our approach, the classification is performed by seeking a categorization that minimizes thermodynamic free energy which is defined as the sum of classification errors and entropy. The error is calculated as the squared sum of the value differences between events. The classification depends on the noise parameter T that represents the strength of the intrinsic error in the observation and classification process. The classification results would be applicable in space weather forecasting.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.49-55
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• 2016

In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.497-503
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• 2011

In this paper, we proposed the MCE as a GMM training method to improve the performance of environmental sounds recognition. We model the environmental sounds data with newly defined misclassification function using the log likelihood of the corresponding class and the log likelihood of the rest classes for discriminative training. The model parameters are estimated with the loss function using GPD(generalized probabilistic descent). For recognition performance comparison, we extracted the 12 degrees features using preprocessing and MFCC(mel-frequency cepstral coefficients) of the 9 kinds of environmental sounds and carry out GMM classification experiments. According to the experimental results, MCE training method showed the best performance by an average of 87.06% with 19 mixtures. This result confirmed us that MCE training method could be effectively used as a GMM training method in environmental sounds recognition.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.619-630
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• 2011

In order to estimate an appropriate threshold and evaluate its performance for the data mixed with two different distributions, nine kinds of well-known classification accuracy measures such as MVD, Youden's index, the closest-to- (0,1) criterion, the amended closest-to- (0,1) criterion, SSS, symmetry point, accuracy area, TA, TR are clustered into five categories on the basis of their characters. In credit evaluation study, it is assumed that the score random variable follows normal mixture distributions of the default and non-default states. For various normal mixtures, optimal cut-off points for classification measures belong to each category are obtained and type I and II error rates corresponding to these cut-off points are calculated. Then we explore the cases when these error rates are minimized. If normal mixtures might be estimated for these kinds of real data, we could make use of results of this study to select the best classification accuracy measure which has the minimum error rate.

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.252-255
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• 2008

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is expressed as the SVM of optimally weighted mel-frequency cepstral coefficients (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that different weights are assigned to each MFCC filter bank which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for gender identification using SVM.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.105-112
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• 2006

In this study, we propose a simple preprocessing method for classification of basal cell carcinoma (BCC), which is one of the most common skin cancer. The preprocessing step consists of data clipping with a half Hanning window and dimension reduction with principal components analysis (PCA). The application of the half Hanning window deemphasizes the peak near $1650cm^{-1}$ and improves classification performance by lowering the false negative ratio. Classification results with various classifiers are presented to show the effectiveness of the proposed method. The classifiers include maximum a posteriori probability (MAP), k-nearest neighbor (KNN), probabilistic neural network (PNN), multilayer perceptron(MLP), support vector machine (SVM) and minimum squared error (MSE) classification. Classification results with KNN involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic BCC detection.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.662-665
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• 1988

This paper proposes the Minimum-Distance Classification(MDC) processor using the Residue Number System(RNS). The proposed MDC Processor in this paper is efficient for real-time pattern clustering application and illustrate satisfiable error rate in application experiments of image segmentation but error rate increase as cluster number do.

• The Journal of the Acoustical Society of Korea
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• v.27 no.8
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• pp.443-449
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• 2008

One of the key issues in practical speech processing is to achieve robust Voice Activity Deteciton (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ equally weighted likelihood ratios (LRs), which, however, deviates from the real observation. Furthermore voice activities in the adjacent frames have strong correlation. In other words, the current frame is highly correlated with previous frame. In this paper, we propose the effective VAD approach based on a minimum classification error (MCE) method which is different from the previous works in that different weights are assigned to both the likelihood ratio on the current frame and the decision statistics of the previous frame.