• Journal of Information Technology Services
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• v.15 no.1
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• pp.153-166
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• 2016

Ensemble classification is an approach that combines individually trained classifiers in order to improve prediction accuracy over individual classifiers. Ensemble techniques have been shown to be very effective in improving the generalization ability of the classifier. But base classifiers need to be as accurate and diverse as possible in order to enhance the generalization abilities of an ensemble model. Bagging is one of the most popular ensemble methods. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. In this study we proposed a new bagging variant ensemble model, Randomized Bagging (RBagging) for improving the standard bagging ensemble model. The proposed model was applied to the bankruptcy prediction problem using a real data set and the results were compared with those of the other models. The experimental results showed that the proposed model outperformed the standard bagging model.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.363-370
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• 2009

In order to populate ontology, this paper presents a generalization method of ontology instances, extracted from texts and web pages, by using unsupervised learning techniques for word sense disambiguation, which uses open APIs and lexical resources such as Google and WordNet. According to the experimental results, our method achieved a 15.8% improvement over the previous research.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.484-484
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• 2000

The problem of determining the proper size of an neural network is recognized to be crucial, especially for its practical implications in such important issues as learning and generalization. Unfortunately, it usually is not obvious what size is best: a system that is too snail will not be able to learn the data while one that is just big enough may learn the slowly and be very sensitive to initial conditions and learning parameters. One popular technique is commonly known as pruning and consists of training a larger than necessary network and then removing unnecessary weights/nodes. In this paper, a new pruning method is developed, based on the penalty-term methods. This method makes the neural network good for the generalization and reduces the retraining time after pruning weights/nodes.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.58-66
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• 2007

This paper introduces the new generic dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system for complex dynamic hydrological modeling tasks. The proposed DNFLMS applies a local generalization principle and an one-pass training procedure by using the evolving clustering method to create and update fuzzy local models dynamically and the extended Kalman filtering learning algorithm to optimize the parameters of the consequence part of fuzzy local models. The proposed DNFLMS is applied to develop the inference model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatts Cobb hydropower station on springs flow. It is demonstrated that the proposed DNFLMS is superior in terms of model accuracy, model complexity, and computational efficiency when compared with a multi-layer perceptron trained with the back propagation learning algorithm and well-known adaptive neural-fuzzy inference system, both of which adopt global generalization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.877-895
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• 2010

The state-of-the-art techniques of storing XML data, modeled as an XML tree, are node-based in the sense that they are centered around XML node labeling and the storage unit is an XML node. In this paper, we propose a generalization of such techniques so that the storage unit is an XML subtree that consists of one or more nodes. Despite several advantages with such generalization, a major problem would be inefficiency in XPath processing where the stored subtrees are to be parsed on the fly in order for the nodes inside them to be accessed. We solve this problem, proposing a technique whereby no parsing of the subtrees involved in XPath processing is needed at all unless they contain the nodes of the final query result. We prove that the correctness of XPath processing is guaranteed with our technique. Through implementation and experiments, we also show that the overhead of our technique is acceptable.

• Journal for History of Mathematics
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• v.29 no.2
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• pp.73-88
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• 2016

To introduce materials related to our traditional mathematics and to reinterpret modernly them could be a good tool to find the cultural values of them. We analyzed the degree for utilization of the history of mathematics in the middle school textbooks developed depending on the 2009 revised mathematics curriculum. Through this, we suggest the need for research on concrete and practical teaching and learning materials development utilizing the history of mathematics. We reinterpret, in modern style based on the curriculum, two subjects dealt with in 'GakDeungByeonHyeongSeupYu', the first theme of 'SanSulGwanGyeon' written by Lee Sang Hyuk. We expansively reconstruct the original samples up to regular decagon so that students might figure out the situation of all regular polygon using a kind of mathematics software GeoGebra. Also this process is constructed on the basis of the curriculum for an implementation the secondary school classes.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.2
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• pp.253-265
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• 2012

Gottlieb polynomials were introduced and investigated in 1938, and then have been cited in several articles. Very recently Khan and Akhlaq introduced and investigated Gottlieb polynomials in two and three variables to give their generating functions. Subse- quently, Khan and Asif investigated the generating functions for the $q$-analogue of Gottlieb polynomials. Also, by modifying Khan and Akhlaq's method, Choi presented a generalization of the Gottlieb polynomials in $m$ variables to give two generating functions of the generalized Gottlieb polynomials ${\varphi}_{n}^{m}(\cdot)$. Here, we aim at defining a $q$-extension of the generalized two variable Gottlieb polynomials ${\varphi}_{n}^{2}(\cdot)$ and presenting their several generating functions.

• Proceedings of the IEEK Conference
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• summer
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• pp.5-9
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• 2004

This paper proposed a systematic analysis for slotted ALOHA with capture effect. This is a generalization for slotted ALOHA system. Based on this model, we can increase the maximum through-put of slotted ALOHA system with two power levels. Lee's algorithm is consider to be an extension of ALOHA system with capture effect. In this paper, we showed that, the choice of Lee's algorithm is not an optimum one. Based on the previous experimental results, we proposed here a more practical analysis for slotted ALOHA system. The result is very accurate and can be applied to other wireless systems which also employed capture effect.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.6
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• pp.71-78
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• 1997

In this paper, a new neural network training algorithm will be devised for function approximator with good generalization characteristics and tested with the time series prediction problem using santaFe competition data sets. To enhance the generalization ability a constraint term of hidden neuraon activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. A hybrid learning algorithm of the error-back propagation and Hebbian learning algorithm with weight decay constraint will be naturally developed by the steepest decent algorithm minimizing the proposed cost function without much increase of computational requriements.

• Speech Sciences
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• v.8 no.3
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• pp.185-200
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• 2001

This study was designed to investigate the treatment, generalization, and maintenance effects of vocal relaxation treatment on compensatory articulation(i.e., glottalization of plosive sound) of three children with cleft palate. Multiple baseline design was applied to evaluate treatment, generalization, and maintenance effects. The targeted phonemes were ph/, th/, /t/ which Were frequently substituted by glottal stop sounds. The main component of the treatment program was vocal relaxation using humming and aspiration sound /h/. The following conclusions were deduced from the results: (1) the treatment program for compensatory articulation was effective in facilitating correct production of targeted phoneme and eliminating glottalization for all subjects, (2) the treatment effects on articulation accuracy were generalized to untreated phonemes (/c/, /c$c^{h}$/) for 2 subjects, (3) the treatment effects on decrease of glottalization were generalized to untreated phonemes for all subjects, and (4) the treatment effects were maintained for all subjects for 2 weeks after treatment was terminated.