• 제목/요약/키워드: semisupervised learning

검색결과 6건 처리시간 0.018초

SVM-KNN-AdaBoost를 적용한 새로운 중간교사학습 방법 (Semisupervised Learning Using the AdaBoost Algorithm with SVM-KNN)

  • 이상민;연준상;김지수;김성수
    • 전기학회논문지
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    • 제61권9호
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    • pp.1336-1339
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    • 2012
  • In this paper, we focus on solving the classification problem by using semisupervised learning strategy. Traditional classifiers are constructed based on labeled data in supervised learning. Labeled data, however, are often difficult, expensive or time consuming to obtain, as they require the efforts of experienced human annotators. Unlabeled data are significantly easier to obtain without human efforts. Thus, we use AdaBoost algorithm with SVM-KNN classifier to apply semisupervised learning problem and improve the classifier performance. Experimental results on both artificial and UCI data sets show that the proposed methodology can reduce the error rate.

Semisupervised support vector quantile regression

  • Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • 제26권2호
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    • pp.517-524
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    • 2015
  • Unlabeled examples are easier and less expensive to be obtained than labeled examples. In this paper semisupervised approach is used to utilize such examples in an effort to enhance the predictive performance of nonlinear quantile regression problems. We propose a semisupervised quantile regression method named semisupervised support vector quantile regression, which is based on support vector machine. A generalized approximate cross validation method is used to choose the hyper-parameters that affect the performance of estimator. The experimental results confirm the successful performance of the proposed S2SVQR.

A transductive least squares support vector machine with the difference convex algorithm

  • Shim, Jooyong;Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • 제25권2호
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    • pp.455-464
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    • 2014
  • Unlabeled examples are easier and less expensive to obtain than labeled examples. Semisupervised approaches are used to utilize such examples in an eort to boost the predictive performance. This paper proposes a novel semisupervised classication method named transductive least squares support vector machine (TLS-SVM), which is based on the least squares support vector machine. The proposed method utilizes the dierence convex algorithm to derive nonconvex minimization solutions for the TLS-SVM. A generalized cross validation method is also developed to choose the hyperparameters that aect the performance of the TLS-SVM. The experimental results conrm the successful performance of the proposed TLS-SVM.

Performance analysis and comparison of various machine learning algorithms for early stroke prediction

  • Vinay Padimi;Venkata Sravan Telu;Devarani Devi Ningombam
    • ETRI Journal
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    • 제45권6호
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    • pp.1007-1021
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    • 2023
  • Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

AI-based language tutoring systems with end-to-end automatic speech recognition and proficiency evaluation

  • Byung Ok Kang;Hyung-Bae Jeon;Yun Kyung Lee
    • ETRI Journal
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    • 제46권1호
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    • pp.48-58
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    • 2024
  • This paper presents the development of language tutoring systems for nonnative speakers by leveraging advanced end-to-end automatic speech recognition (ASR) and proficiency evaluation. Given the frequent errors in non-native speech, high-performance spontaneous speech recognition must be applied. Our systems accurately evaluate pronunciation and speaking fluency and provide feedback on errors by relying on precise transcriptions. End-to-end ASR is implemented and enhanced by using diverse non-native speaker speech data for model training. For performance enhancement, we combine semisupervised and transfer learning techniques using labeled and unlabeled speech data. Automatic proficiency evaluation is performed by a model trained to maximize the statistical correlation between the fluency score manually determined by a human expert and a calculated fluency score. We developed an English tutoring system for Korean elementary students called EBS AI Peng-Talk and a Korean tutoring system for foreigners called KSI Korean AI Tutor. Both systems were deployed by South Korean government agencies.

인용분석에서의 모호한 저자명 식별을 위한 방법들에 관한 고찰 (Review of Author Name Disambiguation Techniques for Citation Analysis)

  • 김현정
    • 한국비블리아학회지
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    • 제23권3호
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    • pp.5-17
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    • 2012
  • 서지 데이터베이스를 이용한 인용분석연구를 진행하기 이전에 이루어져야 할 과정 중 하나가 모호한 저자명의 식별이라고 할 수 있다. 대부분 서지 데이터베이스에는 저자의 성(姓)과 이름의 이니셜만을 표기하는 경우가 많은데, 중국이나 한국 등 아시아 국가 출신의 연구자들은 같은 성을 가진 사람이 매우 많고, 이름의 이니셜까지 같은 경우도 상당히 많아서 이름검색만으로 찾고자 하는 저자를 식별해내기가 쉽지 않기 때문이다. 아시아 국가 출신의 학자들이 유난히 많은 연구분야들에서는 이러한 문제들이 더더욱 큰 문제가 되며, 인용분석 뿐만 아니라 일반적인 정보검색에서도 매우 중요한 요인이 될 수 있다. 모호한 저자명을 식별해내는 방법에는 자동화된 알고리듬을 이용하여 각각의 저자를 식별해내는 방법과 저자 클러스터링을 얻어내기 위해 일일이 수작업으로 데이터셋을 구축하는 방법, 그리고 두 가지 방법을 혼용한 반자동화된 방법 등이 있다. 본 연구는 "모호한 저자명 식별"을 위해 개발된 여러 가지 방법들을 고찰해보기로 한다.