• International Journal of Contents
• /
• v.10 no.1
• /
• pp.23-28
• /
• 2014

When developing a classifier using various objective functions, it is important to compare the performances of the classifiers. Although there are statistical analyses of objective functions for classifiers, simulation results can provide us with direct comparison results and in this case, a comparison criterion is considerably critical. A Receiver Operating Characteristics (ROC) graph is a simulation technique for comparing classifiers and selecting a better one based on a performance. In this paper, we adopt the ROC graph to compare classifiers trained by mean-squared error, cross-entropy error, classification figure of merit, and the n-th order extension of cross-entropy error functions. After the training of feed-forward neural networks using the CEDAR database, the ROC graphs are plotted to help us identify which objective function is better.

• Proceedings of the Korean Statistical Society Conference
• /
• 2003.05a
• /
• pp.85-90
• /
• 2003

In this paper we develop a method for finding optimal ordering of K statistical models. This is based on a dependent paired comparison experimental arrangement whose results can naturally be represented by a completely oriented graph (also so called tournament graph). Introducing preference probabilities, strong transitivity conditions, and an optimal criterion to the graph, we show that a Hamiltonian path obtained from row sum ranking is the optimal ordering. Necessary theories involved in the method and computation are provided. As an application of the method, generalized variances of K multivariate normal populations are compared by a Bayesian approach.

• Proceedings of the Safety Management and Science Conference
• /
• 2006.11a
• /
• pp.85-89
• /
• 2006

The main objective of this study is to propose numerical guideline for the improvement of educational environment about high school girls. In order to analyze feature of the somatotype of the high school girls, analysis of this study was performed about 25 body parts such as height(7 parts), width(4 parts), thickness(4 parts), circumference(5 parts), length(4 parts), and body weight. For the specific comparison on body dementions, Mollison's comparison graph were used.

• Journal of Korea Multimedia Society
• /
• v.6 no.4
• /
• pp.736-745
• /
• 2003

Traditional slicing techniques make slices through dependence graphs. They also improve the accuracy of slices. However, traditional slicing techniques require many vertices and edges in order to express a data communication link because they are based on static slicing techniques. Therefore the graph becomes very complicated, and size of the slices is larger. We propose the representation of a dynamic object-oriented program dependence graph so as to process the slicing of object-oriented programs that is composed of related programs in order to process certain jobs. We also propose an efficient slicing algorithm using the relations of relative tables in order to compute dynamic slices of object-oriented programs. Consequently, the efficiency of the proposed efficient dynamic object-oriented program dependence graph technique is also compared with the dependence graph techniques discussed previously As a result, this is certifying that an efficient dynamic object-oriented program dependence graph is more efficient in comparison with the traditional object-oriented dependence graphs and dynamic object-oriented program dependence graph.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2008.10b
• /
• pp.651-655
• /
• 2008

Traditional slicing techniques make slices through dependence graphs. They also improve the accuracy of slices. However, traditional slicing techniques require many vertices and edges in order to express a data communication links. Therefore the graph becomes complicated, and size of the slices is larger. We propose the representation of a dynamic object-oriented program dependence graph so as to process the slicing of object-oriented programs that is composed of related programs in order to process certain jobs. The efficiency of the proposed efficient dynamic object-oriented program dependence graph technique is also compared with the dependence graph techniques discussed previously. As a result, this is certifying that an efficient dynamic object-oriented program dependence graph is more efficient in comparison with the traditional dynamic object-oriented program dependence graph.

• 한국해양학회지
• /
• v.28 no.1
• /
• pp.72-78
• /
• 1993

Sediment grain size was analysed and compared for standard solids and sediment samples using two different methods; SediGraph and MasterSizer. SediGraph results on sediment samples appeared as finer than those of MasterSizer, and the difference is great especially for biogenic siliceous ooze. The difference is maybe due to the following different points in two methods; pretreatment procedure, sample concentration, detachability on fine grains (about 1 um), and detection principle on nonspherical grains.

• Proceedings of the IEEK Conference
• /
• 2001.06d
• /
• pp.231-234
• /
• 2001

This paper proposes a face recognition technique that effectively combines elastic graph matching (EGM) and Fisherface algorithm. EGM as one of dynamic lint architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, the linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional method, the proposed approach could obtain satisfactory results in the perspectives of recognition rates and speeds. Especially, we could get maximum recognition rate of 99.3% by leaving-one-out method for the experiments with the Yale Face Databases.

• Communications for Statistical Applications and Methods
• /
• v.27 no.5
• /
• pp.569-578
• /
• 2020

Various graph clustering methods have been introduced to identify communities in social or biological networks. This paper studies the entropy-based and the Markov chain-based methods in clustering the undirected graph. We examine the performance of two clustering methods with conventional methods based on quality measures of clustering. For the real applications, we collect the mathematical subject classification (MSC) codes of research papers from published mathematical databases and construct the weighted code-to-document matrix for applying graph clustering methods. We pursue to group MSC codes into the same cluster if the corresponding MSC codes appear in many papers simultaneously. We compare the MSC clustering results based on the several assessment measures and conclude that the Markov chain-based method is suitable for clustering the MSC codes.

• Journal of Korea Multimedia Society
• /
• v.5 no.3
• /
• pp.331-341
• /
• 2002

Traditional slicing techniques make slices through dependence graph and improve the accuracy of slices. However, traditional slicing techniques require many vertices and edges in order to express a data communication link because they are based on static slicing techniques. Therefore the graph becomes very complicated. We propose the representation of a dynamic system dependence graph so as to process the slicing of a software system that is composed of related programs in order to process certain jobs. We also propose programs on efficient slicing algorithm using relations of relative tables in order to compute dynamic slices of a software system. Using a marking table from results of the proposed algorithm can make dynamic system dependence graph for dynamic slice generation. Tracing this graph can generate final slices. We have illustrated our example with C program environment. Consequently, the efficiency of the proposed dynamic system dependence graph technique is also compared with the dependence graph techniques discussed previously. As the results, this is certifying that the dynamic system dependence graph is more efficient in comparison with system dependence graph.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.7
• /
• pp.2568-2584
• /
• 2015

Spectral clustering has become one of the most popular clustering approaches in recent years. Similarity graph constructed on the data is one of the key factors that influence the performance of spectral clustering. However, the similarity graphs constructed by existing methods usually contain some unreliable edges. To construct reliable similarity graph for spectral clustering, an efficient method based on Markov random walk (MRW) is proposed in this paper. In the proposed method, theMRW model is defined on the raw k-NN graph and the neighbors of each sample are determined by the probability of the MRW. Since the high order transition probabilities carry complex relationships among data, the neighbors in the graph determined by our proposed method are more reliable than those of the existing methods. Experiments are performed on the synthetic and real-world datasets for performance evaluation and comparison. The results show that the graph obtained by our proposed method reflects the structure of the data better than those of the state-of-the-art methods and can effectively improve the performance of spectral clustering.