• 대한예방의학회:학술대회논문집
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• 대한예방의학회 1994년도 교수 연수회(역학)
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• pp.150-153
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• 1994

Receiver operating characteristic (ROC) curves have been frequently used to compare probability models applied to medical problems. Though the curves are a measure of the discriminatory power of a model. they do not reflect the model's accuracy. A supplementary accuracy curve is derived which will be coincident with the ROC curve if the model is reliable. will be above the ROC curve if the model's probabilities are too high or below if they are too low. A clinical example of this new graphical presentation is given.

• 한국멀티미디어학회논문지
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• 제22권8호
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• pp.852-859
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• 2019

With the development of IT technology and smart devices, various applications utilizing image information are being developed. In order to provide an intuitive interface for pronunciation recognition, there is a growing need for research on pronunciation recognition using mouth feature values. In this paper, we propose a system to distinguish Korean vowel pronunciations by detecting feature points of lips region in images and applying Bayesian based learning model. The proposed system implements the recognition system based on Bayes' theorem, so that it is possible to improve the accuracy of speech recognition by accumulating input data regardless of whether it is speaker independent or dependent on small amount of learning data. Experimental results show that it is possible to effectively distinguish Korean vowels as a result of applying probability based Bayesian classification using only visual information such as mouth shape features.

• 산업경영시스템학회지
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• 제37권1호
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• pp.1-8
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• 2014

This paper proposes an emotion classifier from EEG signals based on Bayes' theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2002년도 Language, Information, and Computation Proceedings of The 16th Pacific Asia Conference
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• pp.166-176
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• 2002

A homonym could be disambiguated by another words in the context as nouns, predicates used with the homonym. This paper using semantic information (co-occurrence data) obtained from definitions of part of speech (POS) tagged UMRD-S$^1$), In this research, we have analyzed the result of an experiment on a homonym disambiguation system based on statistical model, to which Bayes'theorem is applied, and suggested a model established of the weight of sense rate and the weight of distance to the adjacent words to improve the accuracy. The result of applying the homonym disambiguation system using semantic information to disambiguating homonyms appearing on the dictionary definition sentences showed average accuracy of 98.32% with regard to the most frequent 200 homonyms. We selected 49 (31 substantives and 18 predicates) out of the 200 homonyms that were used in the experiment, and performed an experiment on 50,703 sentences extracted from Sejong Project tagged corpus (i.e. a corpus of morphologically analyzed words) of 3.5 million words that includes one of the 49 homonyms. The result of experimenting by assigning the weight of sense rate(prior probability) and the weight of distance concerning the 5 words at the front/behind the homonym to be disambiguated showed better accuracy than disambiguation systems based on existing statistical models by 2.93%,

• Tuberculosis and Respiratory Diseases
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• 제47권4호
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• pp.498-506
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• 1999

연구배경 : 고립성 폐결절에 대한 접근에 있어 가장 중요한 것은 결절의 악성여부를 결정하는 것이다. 지금까지 고립성 폐결절의 악성여부에 대한 예측은 주로 방사선학적 소견에 의하여 이루어졌으나 수술 전 진단의 정확성은 보고자에 따라 차이가 있다. 수술 전 진단의 부정확은 고립성 폐결절 자체에 대한 진단의 어려움도 있지만 환자의 임상적 방사선학적 특징들을 통합적으로 고려하지 않은 점 역시 중요한 원인 중의 하나이다. 저자들은 Bayes식 접근법을 이용하여 고립성 폐결절 환자의 임상적 특징과 방사선학적 특징들을 통합적으로 고려하여 결절의 악성 확률을 구하여 진단 및 치료방법의 결정에 도움을 주고자 하였다. 대상 및 방법 : 조직학적으로 확진된 고립성 폐결절 180예를 대상으로 임상적 특징과 방사선학적 특징에 대한 Bayes식 접근법으로 결절의 악성 가능성을 후향적으로 조사하였다. 결 과 : 환자의 임상적 특징 중 연령이 증가할수록, 특히 66세 이상인 경우 likelihood ratio가 높았으며(LR 3.64), 46 pack-year 이상의 흡연력이 있는 경우 악성 가능성이 높았다(LR 8.38). 방사선학적 소견 중 결절의 크기가 클수록, 주위 조직과 경계가 불분명하고 엽상이나 극상모양의 결절이 likelihood ratio가 높았다. 결 론 : Likelihood ratio를 이용한 Bayes식 접근법을 이용하여 고립성 폐결절의 악성 확률을 예측하는 것은 특징적인 방사선학적 소견에만 의존하여 결절의 악성 가능성을 예측하는 것보다 더 정확하며, 결절의 진단이나 치료에 대한 방향을 결정을 하는데 유용한 지표로 이용될 수 있을 것으로 생각된다.