KNN-AdaBoost 모델과 AdaBoost-SVM 모델을 통한 준교사학습 비교
semi-supervised Learning compared with KNN-AdaBoost model and AdaBoost-SVM model

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The supervised-learning and unsupervised-learning when analyzed in the case of the one without as there are output information is combined data, the error rate is not easily trust that a comparison learning history. Therefore I suggest the semi-supervised- learning that gave me that were mixed compared to history unsupervised-learning and supervised-learning. In addition, the semi-supervised learning that gave are mutually comparing model that combines the model and AdaBoost and SVM bound the KNN algorithm and AdaBoost algorithm, to check the semi-supervised learning of the error rate is low binding model.

Abstract

Ⅰ. 서론

Ⅱ. 관련연구

Ⅲ. 모의실험

Ⅳ. 결론

참고문헌

KNN-AdaBoost 모델과 AdaBoost-SVM 모델을 통한 준교사학습 비교
semi-supervised Learning compared with KNN-AdaBoost model and AdaBoost-SVM model

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KNN-AdaBoost 모델과 AdaBoost-SVM 모델을 통한 준교사학습 비교 semi-supervised Learning compared with KNN-AdaBoost model and AdaBoost-SVM model

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KNN-AdaBoost 모델과 AdaBoost-SVM 모델을 통한 준교사학습 비교
semi-supervised Learning compared with KNN-AdaBoost model and AdaBoost-SVM model

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