• Korean Journal of Logic
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• v.17 no.3
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• pp.399-424
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• 2014

The main objective of this paper is to examine critically White's claim that there is a conflict between Perceptual Dogmatism and Bayesian Theory of Confirmation. For this purpose, this paper is structured as follows: In Section 2, I will introduce White's argument. Section 3 is dedicated to explaining some elements of Bayesian Theory of Confirmation. In particular, I will provide an explanation of confirmation measures and Bayesian Favoring. Using these two conceptual apparatuses, it will be shown that, contrary to what White has thought, there is a way of supporting Perceptual Dogmatism by means of Bayesian Theory of Confirmation - in particular, Bayesian Theory of Favoring.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.857-868
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• 2014

Data mining is the representative analysis methodology in the era of big data, and is the process to analyze a massive volume database and summarize it into meaningful information. Association rule technique finds the relationship among several items in huge database using the interestingness measures such as support, confidence, lift, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. Moreover, we can not know association direction by them. This paper propose causally confirmed association thresholds to compensate for these problems, and then check the three conditions of interestingness measures. The comparative studies with basic association thresholds, causal association thresholds, and causally confirmed association thresholds are shown by simulation studies. The results show that causally confirmed association thresholds are better than basic and causal association thresholds.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1189-1197
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• 2013

Data mining is the process of analyzing a huge database from different perspectives and summarizing it into useful information. One of the well-studied problems in data mining is association rule generation. Association rule mining finds the relationship among several items in massive volume database using the interestingness measures such as support, confidence, lift, etc. Typical applications for this technique include retail market basket analysis, item recommendation systems, cross-selling, customer relationship management, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. This paper propose causal association thresholds to compensate for this problem, and then check the three conditions of interestingness measures. The comparative studies with basic and causal association thresholds are shown by numerical example. The results show that causal association thresholds are better than basic association thresholds.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.83-90
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• 2010

The number of searched nodes and the computation time in an index should be minimized for optimizing the processing cost of the nearest neighbor query. The Measurement of search distance considered a circular location property of objects is required to accurately select the nodes which will be searched in the nearest neighbor query. In this paper, we propose the processing method of the nearest neighbor query be considered a circular location property of object where the search space consists of a circular domain and show its performance by experiments. The proposed method uses the circular minimum distance and the circular optimal distance which are the search measurements for optimizing the processing cost of the nearest neighbor query.

• Fire Science and Engineering
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• v.10 no.3
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• pp.19-28
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• 1996

This paper present the way the fire detector determines whether a fire has broken out or not using the fuzzy measure. This method is based on Dempster's combination rule using the belief measure. The detector indicate a 'Fire'(F) or 'Nonfire'(N) when it determines whether a fire has broken out or not. To determine this, the fuzzy rule is applied in the setting value for the heat and smoke detector which is used. As a result, It is proved that the final decision can be determined more exactly whether a fire has broken out or not in proportion to the frequency of the fuzzy measure and the value of Bel (F).

• Korean Journal of Logic
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• v.22 no.3
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• pp.351-395
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• 2019

Hempel's original intuitions about the raven's paradox are summed up in three ways. The first is known as the paradoxical conclusion: If one observes that an object a - about which nothing is antecedently known - is a non-black non-raven, then this observation confirms that all ravens are black. The second is an intuitive verdict of the misled conclusion of the paradox: If one observes that an object a - which is known to be a non-raven - is non-black (hence, is a non-black non-raven), then this observation does not confirmationally affect that all ravens are black. The third is a comparative claim between the two intuitions: the degree of confirmation appearing in the first intuition is greater than the degree of confirmation in the second intuition. The Standard Bayesian Solution of the paradox is evaluated to fleshed Hempel's intuitions out by establishing the first intuition. However, such an evaluation of this solution should be further analyzed because Hempel's intuition is not the only one. The solution of paradox does not establish the second intuition in a strict sense. However, I think the Bayesian solution will establish the second intuition based on its typical strategy of quantitative vindication. If only quantitative vindication is accepted, this evaluation of the solution remains valid. Nevertheless, the solution fails to establish the third intuition. In this article, I propose a new way to apply the Bayesian method to establish Hempel's intuitions, including the third intuition. If my analysis is correct, the Standard Bayesian Solution of the raven's paradox could indeed flesh Hempel's intuitions out by denying one of the assumptions considered essential.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.32-41
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• 1999

In spatial database system, the nearest neighbor query occurs frequently and requires the processing cost higher than other spatial queries do. The number of nodes to be searched in the index can be minimized for optimizing the cost of processing the nearest neighbor query. The optimal search distance is pr9posed for the measurement of a search distance to accurately select the nodes which will be searched in the nearest neighbor query. In this paper, we prove properties of the optimal search distance in N-dimensional. We show through experiments that the performance of query processing of our method is superior to other method using maximum search distance.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.239-254
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• 2021

This paper studies time series analysis with estimation and forecasting for Korean COVID-19 confirmed cases, based on the approach of a heterogeneous autoregressive (HAR) model with two-piece t (TP-T) distributed errors. We consider HAR-TP-T time series models and suggest a step-by-step method to estimate HAR coefficients as well as TP-T distribution parameters. In our proposed step-by-step estimation, the ordinary least squares method is utilized to estimate the HAR coefficients while the maximum likelihood estimation (MLE) method is adopted to estimate the TP-T error parameters. A simulation study on the step-by-step method is conducted and it shows a good performance. For the empirical analysis on the Korean COVID-19 confirmed cases, estimates in the HAR-TP-T models of order p = 2, 3, 4 are computed along with a couple of selected lags, which include the optimal lags chosen by minimizing the mean squares errors of the models. The estimation results by our proposed method and the solely MLE are compared with some criteria rules. Our proposed step-by-step method outperforms the MLE in two aspects: mean squares error of the HAR model and mean squares difference between the TP-T residuals and their densities. Moreover, forecasting for the Korean COVID-19 confirmed cases is discussed with the optimally selected HAR-TP-T model. Mean absolute percentage error of one-step ahead out-of-sample forecasts is evaluated as 0.0953% in the proposed model. We conclude that our proposed HAR-TP-T time series model with optimally selected lags and its step-by-step estimation provide an accurate forecasting performance for the Korean COVID-19 confirmed cases.

• Food Science and Preservation
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• v.14 no.4
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• pp.414-418
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• 2007

The physicochemical characteristics and sensory properties of Deodeok wine, formed by leaching of Deodeok at room temperature for 180 days, were investigated over the following range of Deodeok levels: 10, 15 and 20% (all w/v). The higher the level of Deodeok, the greater were the final values of total sugars, reducing sugars, total polyphenols, and crude saponins. The Hunter's b-value (yellowness) of Deodeok wine varied markedly with Deodeok levels, and yellowness was highest in Deodeok wine containing 20% (w/v) Deodeok. Non-volatile compounds, that form the basis of the liquor tax law, were 0.64, 1.38 and 2.11% (all w/v), respectively, at day 160. Of these values, that of 2.11% (w/v), the level of non-volatile compounds in Deodeok wine containing 20% (w/v) Deodeok, was in accord with the liquor tax law (that requires this figure to be 2.0%). Sensory evaluation showed that Deodeok wine containing 20% (w/v) Deodeok was superior to the other wines tested.