• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.52-57
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• 2016

The backscatter signals are very weak so they can be easily interfered by signal interferences and channels. In this paper, we propose a two-level classification algorithm for backscatter communications which chooses the idle frequency channel based on cognitive radio systems. The two-level classification algorithm provides an optimal idle frequency channel by obtaining informations about idle frequencies, fading of the channels, and the channels' usage state by primary users. Our simulation results show the improvement of BER and received power performance in backscatter communications by using the proposed algorithm, and the improvement of the algorithm's performance in backscatter communications.

• The Journal of Pediatrics of Korean Medicine
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• v.16 no.2
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• pp.111-128
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• 2002

Objectives : The aim of this study was to investigate the classification methods of the cause of Allergic Rhinitis for Children. Methods : We surveyed the oriental & western medical book concerning the Allergic Rhinitis for Children. Results : 1. The Oriental medicine, Allergic Rhinitis is belong to the BiGu, BunChe and the symptoms are watery rhinorrhea, sneezing and nasal obstruction. 2. The cause of disease is the weak of lung, spleen and kidney, and invasion in to nasal cavity of Poong Han etc a wrong air. 3. In children, the cause of disease is the weak of lung and spleen. and the aim of the treatment is helping the vital energy and expelling the vice.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.166-171
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• 2013

The method proposed in this paper can improve the performance of the Boosting algorithm in machine learning. The proposed Boundary AdaBoost algorithm can make up for the weak points of Normal binary classifier using threshold boundary concepts. The new proposed boundary can be located near the threshold of the binary classifier. The proposed algorithm improves classification in areas where Normal binary classifier is weak. Thus, the optimal boundary final classifier can decrease error rates classified with more reasonable features. Finally, this paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Boundary AdaBoost in a simulation experiment of pedestrian detection using 10-fold cross validation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.22-28
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• 2010

Adaboost is well known for a representative learner as one of the kernel methods. Adaboost which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, Adaboost is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with Adaboost. One-Vs-All and Pair-Wise have been applied to solve the multi-class classification problem, which is one of the multi-class problems. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. However, two methods cannot show good performance. In this paper, we propose the method to solve a multi-target classification problem by using radial basis function of Adaboost weak classifier.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.443-451
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• 2015

In this paper we suggest a new two-wheelers detection algorithm using uniform local binary pattern weighting value for projection vectors. The first, we calculate feature vectors using projection method which has robustness for rotation invariant and reducing dimensionality for each cell from origin image. The second, we applied new weighting values which are calculated by the modified local binary pattern showing the fast compute and simple to implement. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• Culinary science and hospitality research
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• v.23 no.6
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• pp.143-152
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• 2017

This paper examined seemingly well-working compromises in identity construction, questioning whether the compromises could function only nominally in practice. The literature has paid attention to the conflicts which end up functionally sense-making, through either unilaterally enforced or mutually assimilated compromises. In contrast, this paper's analysis of Super Tuscan wines under the Italian government's quality regulation illustrated that the compromises between wineries and classification systems do not work well and make the classification systems meaningless in the end. This study thus argued that compromises in identity construction do not always result in functionally sense-making outcomes: they could be only nominal. This study suggested that idiosyncratic institutional contexts, such as weak organizational legacy, affect the results of identity construction in functional terms. At last, the theoretical and practical implications both in organization and management of this study were well discussed.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1302-1312
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• 2015

Recent research has been devoted and focused on detecting pedestrian and vehicle in intelligent vehicles except for the vulnerable road user(VRUS). In this paper suggest a new projection method which has robustness for rotation invariant and reducing dimensionality for each cell from original image to detect two-wheeler. We applied new weighting values which are calculated by maximum curvature containing very important object shape features and uniform local binary pattern to remove the noise. This paper considered the Adaboost algorithm to make a strong classification from weak classification. Experiment results show that the new approach gives higher detection accuracy than of the conventional method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.477-480
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• 2018

Viola and Jones object detection algorithm is a representative face detection algorithm. The algorithm uses Haar-like features for face expression and uses a cascade-Adaboost algorithm consisting of strong classifiers, a linear combination of weak classifiers for classification. This algorithm requires several parameter settings for its implementation and the set values affect its performance. This paper analyzes face detection performance according to the parameters set in the algorithm.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.355-362
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• 2016

Ensemble methods often help increase prediction ability in various predictive models by combining multiple weak learners and reducing the variability of the final predictive model. In this work, we demonstrate that ensemble methods also enhance the accuracy of prediction under kernel ridge regression and kernel logistic regression classification. Here we apply bagging and random forests to two kernel-based predictive models; and present the procedure of how bagging and random forests can be embedded in kernel-based predictive models. Our proposals are tested under numerous synthetic and real datasets; subsequently, they are compared with plain kernel-based predictive models and their subsampling approach. Numerical studies demonstrate that ensemble approach outperforms plain kernel-based predictive models.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.258-267
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• 2013

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.