• Title/Summary/Keyword: 분할 학습 알고리즘

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Effect of Learning a Divide-and-conquer Algorithm on Creative Problem Solving (분할 정복 알고리즘 학습이 창의적 문제 해결에 미치는 효과)

  • Kim, Yoon Young;Kim, Yungsik
    • The Journal of Korean Association of Computer Education
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    • v.16 no.2
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    • pp.9-18
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    • 2013
  • In secondary education, learning a computer science subject has the purpose to improve creative problem solving ability of students by learning computational thinking and principles. In particular, learning algorithm has been emphasized for this purpose. There are studies that learning algorithm has the effect of creative problem solving based on the leading studies that learning algorithm has the effect of problem solving. However, relatively the importance of the learning algorithm can weaken, because these studies depend on creative problem solving model or special contents for creativity. So this study proves that learning algorithm has the effect of creative problem solving in the view that common problem solving and creative problem solving have the same process. For this, analogical reasoning was selected among common thinking skills and divide-and-conquer algorithm was selected among abstractive principles for analogical reasoning in sorting algorithm. The frequency which solves the search problem by using the binary search algorithm was higher than the control group learning only sequence of sorting algorithm about the experimental group learning divide-and-conquer algorithm. This result means that learning algorithm including abstractive principle like divide-and-conquer has the effect of creative problem solving by analogical reasoning.

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Data Segmentation System using Greedy Algorithm (Greedy 알고리즘을 사용한 데이터 분할 시스템)

  • Kim, Min-Woo;Kim, Se-Jun;Lee, Byung-Jun;Kim, Kyung-Tae;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2018.07a
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    • pp.211-212
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    • 2018
  • 머신 러닝 환경에서 많은 양의 데이터를 한꺼번에 학습하게 되면 데이터 트래픽이 증가함에 따라 흐름 정체가 발생하고 학습 품질이 저하되며 학습속도 지연 등의 문제가 발생한다. 본 연구는 머신러닝 환경에서 빅 데이터 학습 데이터 분할을 위한 핵심 목표인 Greedy 알고리즘에 대해 설명하고 간단한 Greedy 알고리즘을 사용하여 각각의 데이터 파티션을 생성하여 학습 속도의 효율성을 향상시키는 방법을 제안한다.

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Ensemble Learning Algorithm of Specialized Networks (전문화된 네트워크들의 결합에 의한 앙상블 학습 알고리즘)

  • 신현정;이형주;조성준
    • Proceedings of the Korean Information Science Society Conference
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    • 2000.10b
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    • pp.308-310
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    • 2000
  • 관찰학습(OLA: Observational Learning Algorithm)은 앙상블 네트워크의 각 구성 모델들이 아른 모델들을 관찰함으로써 얻어진 가상 데이터와 초기에 bootstrap된 실제 데이터를 학습에 함께 이용하는 방법이다. 본 논문에서는, 초기 학습 데이터 셋을 분할하고 분할된 각 데이터 셋에 대하여 앙상블의 구성 모델들을 전문화(specialize)시키는 방법을 적용하여 기존의 관찰학습 알고리즘을 개선시켰다. 제안된 알고리즘은 bagging 및 boosting과의 비교 실험에 의하여, 보다 적은 수의 구성 모델로 동일 내지 보다 나은 성능을 나타냄이 실험적으로 검증되었다.

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Ensemble of Specialized Networks based on Input Space Partition (입력공간 분담에 의한 네트워크들의 앙상블 알고리즘)

  • 신현정;이형주;조성준
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2000.10a
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    • pp.33-36
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    • 2000
  • 관찰학습(OLA: Observational Learning Algorithm)은 앙상블 네트워크의 각 구성 모델들이 다른 모델들을 관찰함으로써 얻어진 가상 데이터와 초기에 bo otstrap된 실제 데이터를 학습에 함께 이용하는 방법이다. 본 논문에서는, 초기 학습 데이터 셋을 분할하고 분할된 각 데이터 셋에 대하여 앙상블의 구성 모델들을 전문화(specialize)시키는 방법을 적용하여 기존의 관찰학습 알고리즘을 개선시켰다. 제안된 알고리즘은 bagging 및 boosting과의 비교실험에 의하여, 보다 적은 수의 구성 모델로 동일 내지 보다 나은 성능을 나타냄이 실험적으로 검증되었다.

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Defect Diagnostics of Gas Turbine with Altitude Variation Using Hybrid SVM-Artificial Neural Network (SVM-인공신경망 알고리즘을 이용한 고도 변화에 따른 가스터빈 엔진의 결함 진단 연구)

  • Lee, Sang-Myeong;Choi, Won-Jun;Roh, Tae-Seong;Choi, Dong-Whan
    • Journal of the Korean Society of Propulsion Engineers
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    • v.11 no.1
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    • pp.43-50
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    • 2007
  • In this study, Hybrid Separate Learning Algorithm(SLA) consisting of Support Vector Machine(SVM) and Artificial Neural Network(ANN) has been used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine in the off-design range considering altitude variation. Although the number of teaming data and test data highly increases more than 6 times compared with those required for the design condition, the proposed defect diagnostics of gas turbine engine using SLA was verified to give the high defect classification accuracy in the off-design range considering altitude variation.

Optimization of Deep Learning Model Based on Genetic Algorithm for Facial Expression Recognition (얼굴 표정 인식을 위한 유전자 알고리즘 기반 심층학습 모델 최적화)

  • Park, Jang-Sik
    • The Journal of the Korea institute of electronic communication sciences
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    • v.15 no.1
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    • pp.85-92
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    • 2020
  • Deep learning shows outstanding performance in image and video analysis, such as object classification, object detection and semantic segmentation. In this paper, it is analyzed that the performances of deep learning models can be affected by characteristics of train dataset. It is proposed as a method for selecting activation function and optimization algorithm of deep learning to classify facial expression. Classification performances are compared and analyzed by applying various algorithms of each component of deep learning model for CK+, MMI, and KDEF datasets. As results of simulation, it is shown that genetic algorithm can be an effective solution for optimizing components of deep learning model.

Defect Diagnostics of Gas Turbine Engine with Altitude Variation Using SVM and Artificial Neural Network (SVM과 인공신경망을 이용한 고도 변화에 따른 가스터빈 엔진의 결함 진단 연구)

  • Lee Sang-Myeong;Choi Won-Jun;Roh Tae-Seong;Choi Dong-Whan
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2006.05a
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    • pp.209-212
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    • 2006
  • In this study, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. Effect of altitude variation on the Defect Diagnostics algorithm has been included and evaluated. Separate learning Algorithm(SLA) suggested with ANN to loam the performance data selectively after classifying the position of defects by SVM improves the classification speed and accuracy.

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Regression Neural Networks for Improving the Learning Performance of Single Feature Split Regression Trees (단일특징 분할 회귀트리의 학습성능 개선을 위한 회귀신경망)

  • Lim, Sook;Kim, Sung-Chun
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.1
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    • pp.187-194
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    • 1996
  • In this paper, we propose regression neural networks based on regression trees. We map regression trees into three layered feedforward networks. We put multi feature split functions in the first layer so that the networks have a better chance to get optimal partitions of input space. We suggest two supervised learning algorithms for the network training and test both in single feature split and multifeature split functions. In experiments, the proposed regression neural networks is proved to have the better learning performance than those of the single feature split regression trees and the single feature split regression networks. Furthermore, we shows that the proposed learning schemes have an effect to prune an over-grown tree without degrading the learning performance.

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A Memory-based Reasoning Algorithm using Adaptive Recursive Partition Averaging Method (적응형 재귀 분할 평균법을 이용한 메모리기반 추론 알고리즘)

  • 이형일;최학윤
    • Journal of KIISE:Software and Applications
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    • v.31 no.4
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    • pp.478-487
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    • 2004
  • We had proposed the RPA(Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. That algorithm worked not bad in many area, however, the major drawbacks of RPA are it's partitioning condition and the way of extracting major patterns. We propose an adaptive RPA algorithm which uses the FPD(feature-based population densimeter) to stop the ARPA partitioning process and produce, instead of RPA's averaged major pattern, optimizing resulting hyperrectangles. The proposed algorithm required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the RPA. Also, by reducing the number of stored patterns, it showed an excellent results in terms of classification when we compare it to the k-NN.

Image Segmentation by Cascaded Superpixel Merging with Privileged Information (단계적 슈퍼픽셀 병합을 통한 이미지 분할 방법에서 특권정보의 활용 방안)

  • Park, Yongjin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.9
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    • pp.1049-1059
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    • 2019
  • We propose a learning-based image segmentation algorithm. Starting from super-pixels, our method learns the probability of merging two regions based on the ground truth made by humans. The learned information is used in determining whether the two regions should be merged or not in a segmentation stage. Unlike exiting learning-based algorithms, we use both local and object information. The local information represents features computed from super-pixels and the object information represent high level information available only in the learning process. The object information is considered as privileged information, and we can use a framework that utilize the privileged information such as SVM+. In experiments on the Berkeley Segmentation Dataset and Benchmark (BSDS 500) and PASCAL Visual Object Classes Challenge (VOC 2012) data set, out model exhibited the best performance with a relatively small training data set and also showed competitive results with a sufficiently large training data set.