• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.271-279
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• 2001

Sequential pattern discovery is one of main interests in web usage mining. the technique of sequential pattern discovery attempts to find inter-session patterns such that the presence of a set of items is followed by another item in a time-ordered set of server sessions. In this paper, a tree-based sequential pattern finding method is proposed in order to discover navigation patterns in server sessions. At each learning process, the suggested method learns about the navigation patterns per server session and summarized into the modified Rymon's tree.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.466-469
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• 1996

In this paper, we present the global minimization condition by an informal analysis of the Langevine competitive learning neural network. From the viewpoint of the stochastic process, it is important that competitive learning guarantees an optimal solution for pattern recognition. By analysis of the Fokker-Plank equation for the proposed neural network, we show that if an energy function has a special pseudo-convexity, Langevine competitive learning can find the global minima. Experimental results for pattern recognition of handwritten numeral data indicate the superiority of the proposed algorithm.

• Journal of Korea Game Society
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• v.9 no.6
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• pp.117-125
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• 2009

This paper describes the result of applying neuro-fuzzy reasoning, which conducts Go term knowledge based on pattern knowledge, to the opening game of Go. We discuss the implementation of neuro-fuzzy reasoning for deciding the best next move to proceed through the opening game. We also let neuro-fuzzy reasoning play against TD($\lambda$) learning to test the performance. The experimental result reveals that even the simple neuro-fuzzy reasoning model can compete against TD($\lambda$) learning and it shows great potential to be applied to the real game of Go.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.407-408
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• 2007

In this paper, we present a new method for classifying object using LVQ (Learning Vector Quantization) with flexible output layer. The proposed LVQ is a supervised learning method that dynamically generates output neurons and initializes automatically the weight vectors from training patterns. If the classes of the nearest output neuron is different from the class of the training pattern, a new output neuron is created and the given training pattern is used to initialize the weight vector of the created neuron. The proposed method is significantly different from the previous competitive learning algorithms in the point that the output neurons are dynamically generated during the learning process.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.36-39
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• 2004

In this paper, a method of improving the learning speed and convergence rate is proposed to exploit the advantages of artificial neural networks and neuro-fuzzy systems. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for practical image application. As a result of experiment, it does not always guarantee convergence. However, the network showed considerable improvement in learning time and has a high convergence rate. The proposed network can be extended to any number of layers. When we consider only the case of the single layer, the networks had the capability of high speed during the learning process and rapid processing on huge images.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.4
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• pp.2752-2759
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• 1996

This paper presents crack growth analysis approach on the basis of neural networks, a branch of cognitive science to high temperature low cycle fatigue that shows strong nonlinearity in material behavior. As the number of data patterns on crack growth increase, pattern classification occurs well and two point representation scheme with gradient of crack growth curve simulates crack growth rate better than one point representation scheme. Optimal number of learning data exists and excessive number of learning data increases estimated mean error with remarkable learning time J-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).

• The KIPS Transactions:PartA
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• v.11A no.2
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• pp.213-222
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• 2004

Today's Web-based teaching-learning is developing in the direction that learners select and organize the contents, time and order of learning by themselves. That is, it is evolving to provide teaching-learning environment adaptive to individual learners' characteristics(their level of knowledge, pattern of study. areas of interest). This study analyzed learners' learning paths among the variables of learners' characteristics considered important in Web-based teaching- learning process using the Apriori algorithm and grouped learners who had similar learning paths. Based on the result, the author designed and developed a learning-path individualization system In order to provide learners with learning paths, Interface, the progress of learning etc. The proposed system is expected to provide optimal learning environment fit for learners' pattern of study and to be enhancing individual learner's learning effects

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.269-278
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• 2006

This paper presents the new hybrid data mining technique using error pattern, modeling of improving classification accuracy. The proposed method improves classification accuracy by combining two different supervised learning methods. The main algorithm generates error pattern modeling between the two supervised learning methods(ex: Neural Networks, Decision Tree, Logistic Regression and so on.) The Proposed modeling method has been applied to the simulation of 10,000 data sets generated by Normal and exponential random distribution. The simulation results show that the performance of proposed method is superior to the existing methods like Logistic regression and Discriminant analysis.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• Journal of the Korean Society of Earth Science Education
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• v.7 no.2
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• pp.276-285
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• 2014

The purpose of this study is to investigate the pattern and characteristics of elementary school teaching and learning processes in science based classes. The study participants' class was recorded in video and instructional conversation transcription. The pattern of the observed class was analyzed using the classification frame suggested by Mogan &Saxton(2006). In result, the questioning for elicit information was most frequent and questioning for shape understanding and the questioning for press for reflection in its priority. In result, the presence of elicited questioning for the attainment of knowledge and understanding is more prominent in science-based classrooms. It was revealed that the participating teachers used the questioning sentence pattern more frequently and the self-sustained inquiry that accelerates creative thinking of the student was lacking. It was discovered that teaching elicited questioning, which accelerates creative thinking, as well as fact confirmation pattern is a necessary element of training for teachers.