• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.242-246
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• 1993

예측신경회로망 모델은 다층 퍼셉트론을 연속되는 음성특징 벡터간의 비선형예측기로 사용하는 동적인 음성인식 모델이다. 이 모델은 음성의 동적인 특성을 인식에 이용하고 연속음성인식으로의 확장이 용이한 우수한 인식 모델이다. 그러나, 예측신경회로망 모델은 음운학적으로 유사한 음성구간에서의 변별력이 낮다는 문제점이 있다. 그것은 기존의 학습 알고리즘이 다른 어휘와의 거리는 고려하지 않고 대상어휘의 예측오차만 최소화시키기 때문이다. 따라서, 본 논문에서는 직접 인식오차를 최소화시키는 GPD알고리즘에 의해 유사어휘간의 거리를 고려하는 변별력있는 학습 알고리즘을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.235-237
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• 2000

본 논문에서는 환경의 변화에 민감한 시계열 데이터인 주가의 변동과정을 보다 효과적으로 설명하기 위한 방안의 하나로 강화 학습 모형의 도입을 제안하며, 특정 시점의 주가 추세를 평가하는 기준으로 가치도 함수를 채택한다. 가치도 함수의 계산에는 강화 학습 알고리즘의 일종인 Monte Carlo 알고리즘을 적용하고, 신경망 구현에 의해 구한 근사 가치도의 적절성을 평가하였다. 실험 결과로 볼 때, 여타 강화 학습 알고리즘을 추가적으로 적용할 경우, 주가 변동의 시계열적 특성을 더욱 잘 반영할 수 있을 것으로 판단된다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.157-161
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• 2003

본 논문에서는 차량의 주행경로 최적화를 위해 강화학습 개념을 적용하고자 한다. 강화학습의 특징은 관심 대상에 대한 구체적인 지배 규칙의 정보 없이도 최적화된 행동 방식을 학습시킬 수 있는 특징이 있어서, 실제 차량의 주행경로와 같이 여러 교통정보 및 시간에 따른 변화 등에 대한 복잡한 고려가 필요한 시스템에 적합하다. 또한 학습을 위한 강화(보상, 벌칙)의 정도 및 기준을 조절해 즘으로써 다양한 최적주행경로를 제공할 수 있다. 따라서, 본 논문에서는 강화학습 알고리즘을 이용하여 다양한 최적주행경로를 제공해 주는 시스템을 구현한다.

• Journal of The Korean Association of Information Education
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• v.16 no.4
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• pp.489-502
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• 2012

Today computer education puts an emphasis on algorithm education. There are little researches about how to express the given problem in algorithm and how to interpret the expressed algorithm. In this study play-based learning methods dealing with flow chart which is one of the expressing tools of algorithm are developed for lower graders of elementary school. Then we diagnosed the learning possibility of the tool after applying the methods in a classroom environment. There are four types of learning game activities; sequential play, selective play, repetitive play and puzzle play. Puzzle play is a game that students need to reconstruct the learned content to a real flow chart by using flow chart cards. The result of an achievement test after teaching students flow chart showed that the group who took the play-based lesson got their average score with about 7.5% higher than the group who took the ICT-based lesson. Both the groups got their average scroe of more than 9 out of 10 after the lesson. This result shows that flow chart lessons are adaptable for the lower graders of elementary school. It also shows that play-based education can be exceptionally effective.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.13-20
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• 2019

For customized education, it is essential to search the learning path for the learner. The genetic algorithm makes it possible to find optimal solutions within a practical time when they are difficult to be obtained with deterministic approaches because of the problem's very large search space. In this research, based on genetic algorithm, the learning paths to learn 200 chords in 27 music sheets were optimized to maximize the learning effect by balancing and minimizing learner's burden and learning size for each step in the learning paths. Although the permutation size of the possible learning path for 27 learning contents is more than $10^{28}$, the optimal solution could be obtained within 20 minutes in average by an implemented tool in this research. Experimental results showed that genetic algorithm can be effectively used to design complex learning path for customized education with various purposes. The proposed method is expected to be applied in other educational domains as well.

• Journal of the Korea Computer Industry Society
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• v.2 no.2
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• pp.151-156
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• 2001

We describe a new representation for learning concepts that differs from the traditional decision tree and rule induction algorithms. Our algorithm PROLEARN learns one or more prototype per class and follows instance based classification with them. Prototype here differs from psychological term in that we can have more than one prototype per concept and also differs from other instance based algorithms since the prototype is a "ficticious ideal example". We show that PROLEARN is as good as the traditional machine learning algorithms but much move stable than them in an environment that has noise or changing training set, what we call 'stability’.tability’.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.208-209
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• 2011

In this paper, we introduce a Naive Bayes learning algorithm for learning and reasoning in Map-Reduce model based environment. For this purpose, we use Apache Mahout to execute Distributed Naive Bayes on University of California, Irvine (UCI) benchmark data sets. From the experimental results, we see that Apache Mahout' s Distributed Naive Bayes algorithm is comparable to WEKA' s Naive Bayes algorithm in terms of performance. These results indicates that in the future Big Data environment, Map-Reduce model based systems such as Apache Mahout can be promising for machine learning usage.

• Journal of Internet Computing and Services
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• v.4 no.5
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• pp.69-76
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• 2003

In this paper we show a new learning algorithm for pattern classification. This algorithm considered a scheme to find a solution to a problem of incremental learning algorithm when the structure becomes too complex by noise patterns included in learning data set. Our approach for this problem uses a pruning method which terminates the learning process with a predefined criterion. In this process, an iterative model with 3 layer feedforward structure is derived from the incremental model by an appropriate manipulations. Notice that this network structure is not full-connected between upper and lower layers. To verify the effectiveness of pruning method, this network is retrained by EBP. From this results, we can find out that the proposed algorithm is effective, as an aspect of a system performence and the node number included in network structure.

• 한국정보교육학회:학술대회논문집
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• 2010.01a
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• pp.295-299
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• 2010

빠르게 변화하는 지식정보화사회에서 창의적인 사고력을 갖춘 인재를 육성하는 일은 우리 교육의 핵심과제라 할 수 있다. 특히 컴퓨터 지도 영역 중 알고리즘은 프로그래밍의 근간이 되며 창의적인 문제해결력과 사고력을 향상시킬 수 있는 영역으로 그 중요성이 매우 크다. 이에 현행 학교 컴퓨터 교육도 응용프로그램 활용이나 기능 습득위주의 교육에서 벗어나 컴퓨터 원리, 알고리즘, 프로그래밍과 같은 컴퓨터 자체에 대한 교육을 통해 학습자의 문제해결력 및 창의적인 사고력을 신장시켜야 한다. 따라서 본 연구에서는 초등학생에게 적합한 알고리즘 학습내용을 선정하여 학습 프로그램을 개발하고, 이를 통한 학습이 창의적 사고력 신장에 어떠한 영향을 효과를 미치는 가를 알아보고자 한다.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.62-67
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• 2002

From the view point of VLSI implementation, a new teaming algorithm suited for network with quantized connection weights is desired. This paper presents a new teaming algorithm for the DBM(deterministic Boltzmann machine) network with quantized connection weight. The performance of proposed algorithm is tested with the 2-input XOR problem and the 3-input parity problem through computer simulations. The simulation results show that our algorithm is efficient for quantized connection neural networks.