• Journal of Korean Society of Forest Science
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• v.58 no.1
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• pp.48-53
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• 1982

This experiment was carried out to distinguish between picea abies (L.) Karsten and Picea koraiensis Nak by the method of discriminant analysis which is used the metrical continuous characteristic on current inorphological plant taxanomy. The results are summarized as follows 1) The discriminant function and discriminant region from the experiment are Z(x)=Z($x_1,\;x_2$)=$0.000379x_1+0.004354x_2-0.311061$ or Z(x)=Z($x_1,\;x_2$=$0.000379(x_1-60.442800)+0.004354(x_2-66.185100)$, $$R_1=(x{\mid}0.000379x_1+0.004354x_2-0.311061{\geq_-}0)$$, $R_2$=($x{\mid}0.000379x_1+0.004354x_2-0.311061$ <0). 2) The probability of misclassification based on the above discriminant region is P($2{\mid}1$)=$P(1{\mid}2)$=0.444 therefore the probability of simultaneous misclassification of P($2{\mid}1$) and $P(1{\mid}2)$ is about 44.4%. 3) the probability of misclassification by the discriminant function resulted from the experiment if recorded as high but it is thought that there is a considerable meaning to perceive the probability of confidence about the discrimination better than its precision.

• Journal of the Korean Data and Information Science Society
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• v.19 no.1
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• pp.25-36
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• 2008

Error Correcting Output Codes (ECOC) can improve generalization performance when applied to multicategory classification problem. In this study we propose a new criterion to select hyperparameters included in ECOC scheme. Instead of margins of a data we propose to use the probability of misclassification error since it makes the criterion simple. Using this we obtain an upper bound of leave-one-out error of OVA(one vs all) method. Our experiments from real and synthetic data indicate that the bound leads to good estimates of parameters.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1219-1231
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• 2008

The main object of this paper is to develop a leave-one-out(LOO) bound of all pairwise comparison error correcting output codes (APC-ECOC). To avoid using classifiers whose corresponding target values are 0 in APC-ECOC and requiring pilot estimates we developed a bound based on mean misclassification probability(MMP). It can be used to tune kernel hyperparameters. Our empirical experiment using kernel mean squared estimate(KMSE) as the binary classifier indicates that the bound leads to good estimates of kernel hyperparameters.

• Journal of the Korean Institute of Telematics and Electronics
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• v.12 no.1
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• pp.1-6
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• 1975

The boundary has been one of the most important information in radiographic images and the degrees of difficulty involved varies greatly with the quality of the picture. These Buantifications are the means to diagnoses. The purpose of this paper is to quantify intensity variation and the threshold decision which is based on statistical principles and is developed to detect limits in liver scintigrams the entire picture is devide4 into 64 small regions. The kurtosis and variances for each smal region are used as indications to select the histograms the thresholds are computed according to the method o(maximum likelihood which minimizes the probability o( misclassification. Therefore Ive have demonstrated the applicability of the boundary detection and proved good agreement with human recognition, and we can use it for the diagnosis data of liver disease.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.123-130
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• 2009

When we apply MLPs(multilayer perceptrons) to pattern classification problems, we generally allocate one output node for each class and the index of output node denotes a class. On the contrary, in this paper, we propose to increase the number of output nodes per each class for performance improvement of MLPs. For theoretical backgrounds, we derive the misclassification probability in two class problems with additional outputs under the assumption that the two classes have equal probability and outputs are uniformly distributed in each class. Also, simulations of 50 isolated-word recognition show the effectiveness of our method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.1 no.1
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• pp.45-48
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• 1978

If two inspectors classify items in a lot into m classes, it is possible that each of them makes wrong classification in some cases, thus causing bias. Expressions have been obtained for the limits of this bias in estimating the proportion of the different classes. From the results of the classification they obtained limit for the estimates of Proportions have been worked out, based on assumption regarding the magnitudes of probabilities of misclassification. Now we suppose that $P_{ti}{\;}(t=1.2)$ is the probability that t the inspector classifies correctly an item in class $A_i$ and $q_{tji}$ is the probability that he misclassifies in $A_j$ an item actually belonging to $A_i$, therefor, $P_{ti}+ \sum\limits_{j{\neq}i}q_{tji}=1$ An estimate for the proportion $P_k$ of the class $A_k$ in the lot would be $\hat{P}_k=r_{kk}+(\frac{1}{2})\sum\limits_{j{\neq}k}r_{kj}+r_{jk}$ The % Bias in proportion $\hat{P}_k$ is $\frac{E(\hat{P}_k)-P_k}{P_k}{\times}100$

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.11-25
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• 2001

There are several algorithms for classification in modeling relations, patterns, and rules which exist in data. We learn to classify objects on the basis of instances presented to us, not by being given a set of classification rules. The Bayesian learning uses the probability distribution to express our knowledge about unknown parameters and update our knowledge by the law of probability as the evidence gathered from data. Also, the neural network models are designed for predicting an unknown category or quantity on the basis of known attributes by training. In this paper, we compare the misclassification error rates of Bayesian Neural Network method with those of other classification algorithms, CHAID, CART, and QUBST using several data sets.

• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.679-688
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• 2010

Lee (2010) developed a confidence interval for the difference of binomial proportions in two doubly sampled data subject to false-positive errors. The confidence interval seems to be adequate for a general double sampling model subject to false-positive misclassification. However, in many applications, the false-positive error rates could be the same. On this note, the construction of asymptotic confidence interval is considered when the false-positive error rates are common. The coverage behaviors of nine likelihood based confidence intervals are examined. It is shown that the confidence interval based Rao score with the expected information has good performance in terms of coverage probability and expected width.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.1019-1026
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• 1996

A modulation type classifier based on statistical moments has been successfully employed to classify PSK signals. Previously, developed Classifiers were analyzed in AWGN channel only. In this paper, a moments-based modulation type classifier to classify BPSK and QPSK signals over Rayleigh fading channel is proposed and analyzed. The moments of received signal are evaluated with the exact distribution of the received signal and a moments-based classifier is proposed. The performance evaluation of the proposed classifier in terms of the misclassification probability for BPSK and QPSK is investigated under Rayleigh fading environment.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.13-19
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• 2003

Discriminant method using seismic signal was studied for discrimination of surface explosion. By means of the seismic spectral characteristics, multi-variate discriminant analysis was performed. Four single discriminant techniques - Pg/Lg, Lg1/Lg2, Pg1/Pg2, and Rg/Lg - based on seismic source theory were applied to explosion and earthquake training data sets. The Pg/Lg discriminant technique was most effective among the four techniques. Nevertheless, it could not perfectly discriminate the samples of the training data sets. In this study, a compound linear discriminant analysis was defined by using common characteristics of the training data sets for the single discriminants. The compound linear discriminant analysis was used for the single discriminant as an independent variable. From this analysis, all the samples of the training data sets were correctly discriminated, and the probability of misclassification was lowered to 0.7％.