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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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The KIPS Transactions:PartB
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Korea Information Processing Society
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Volume & Issues
Volume 10B, Issue 7 - Dec 2003
Volume 10B, Issue 6 - Oct 2003
Volume 10B, Issue 5 - Aug 2003
Volume 10B, Issue 4 - Aug 2003
Volume 10B, Issue 3 - Jun 2003
Volume 10B, Issue 2 - Apr 2003
Volume 10B, Issue 1 - Feb 2003
Selecting the target year
A Study about Additional Reinforcement in Local Updating and Global Updating for Efficient Path Search in Ant Colony System
Lee, Seung-Gwan ; Chung, Tae-Choong ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 237~242
DOI : 10.3745/KIPSTB.2003.10B.3.237
Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.
Modified Kernel PCA Applied To Classification Problem
Kim, Byung-Joo ; Sim, Joo-Yong ; Hwang, Chang-Ha ; Kim, Il-Kon ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 243~248
DOI : 10.3745/KIPSTB.2003.10B.3.243
An incremental kernel principal component analysis (IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis (KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenspace should be recomputed. IKPCA overcomes these problems by incrementally computing eigenspace model and empirical kernel map The IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the feature extraction and classification problem on nonlinear data set.
Multagent Control Strategy Using Reinforcement Learning
Lee, Hyong-Ill ; Kim, Byung-Cheon ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 249~256
DOI : 10.3745/KIPSTB.2003.10B.3.249
The most important problems in the multi-agent system are to accomplish a goal through the efficient coordination of several agents and to prevent collision with other agents. In this paper, we propose a new control strategy for succeeding the goal of the prey pursuit problem efficiently. Our control method uses reinforcement learning to control the multi-agent system and consider the distance as well as the space relationship between the agents in the state space of the prey pursuit problem.
A Two-Phase Stock Trading System based on Pattern Matching and Automatic Rule Induction
Lee, Jong-Woo ; Kim, Yu-Seop ; Kim, Sung-Dong ; Lee, Jae-Won ; Chae, Jin-Seok ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 257~264
DOI : 10.3745/KIPSTB.2003.10B.3.257
In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.
An Improved Rectangular Decomposition Algorithm for Data Mining
Song, Ji-Young ; Im, Young-Hee ; Park, Dai-Hee ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 265~272
DOI : 10.3745/KIPSTB.2003.10B.3.265
In this paper, we propose a novel improved algorithm for the rectangular decomposition technique for the purpose of performing data mining from large scaled database in a dynamic environment. The proposed algorithm performs the rectangular decompositions by transforming a binary matrix to bipartite graph and finding bicliques from the transformed bipartite graph. To demonstrate its effectiveness, we compare the proposed one which is based on the newly derived mathematical properties with those of other methods with respect to the classification rate, the number of rules, and complexity analysis.
A Collaborative Filtering using SVD on Low-Dimensional Space
Jeong, Jun ; Rhee, Pil-Kyu ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 273~280
DOI : 10.3745/KIPSTB.2003.10B.3.273
Recommender System can help users to find products to Purchase. A representative method for recommender systems is collaborative filtering (CF). It predict products that user may like based on a group of similar users. User information is based on user`s ratings for products and similarities of users are measured by ratings. As user is increasing tremendously, the performance of the pure collaborative filtering is lowed because of high dimensionality and scarcity of data. We consider the effect of dimension deduction in collaborative filtering to cope with scarcity of data experimentally. We suggest that SVD improves the performance of collaborative filtering in comparison with pure collaborative filtering.
Research about feature selection that use heuristic function
Hong, Seok-Mi ; Jung, Kyung-Sook ; Chung, Tae-Choong ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 281~286
DOI : 10.3745/KIPSTB.2003.10B.3.281
A large number of features are collected for problem solving in real life, but to utilize ail the features collected would be difficult. It is not so easy to collect of correct data about all features. In case it takes advantage of all collected data to learn, complicated learning model is created and good performance result can`t get. Also exist interrelationships or hierarchical relations among the features. We can reduce feature`s number analyzing relation among the features using heuristic knowledge or statistical method. Heuristic technique refers to learning through repetitive trial and errors and experience. Experts can approach to relevant problem domain through opinion collection process by experience. These properties can be utilized to reduce the number of feature used in learning. Experts generate a new feature (highly abstract) using raw data. This paper describes machine learning model that reduce the number of features used in learning using heuristic function and use abstracted feature by neural network`s input value. We have applied this model to the win/lose prediction in pro-baseball games. The result shows the model mixing two techniques not only reduces the complexity of the neural network model but also significantly improves the classification accuracy than when neural network and heuristic model are used separately.
Performance Improvement of Collaborative Filtering System Using Associative User′s Clustering Analysis for the Recalculation of Preference and Representative Attribute-Neighborhood
Jung, Kyung-Yong ; Kim, Jin-Su ; Kim, Tae-Yong ; Lee, Jung-Hyun ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 287~296
DOI : 10.3745/KIPSTB.2003.10B.3.287
There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user`s predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn`t regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user`s clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.
The Study on Improvement of Cohesion of Clustering in Incremental Concept Learning
Baek, Hey-Jung ; Park, Young-Tack ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 297~304
DOI : 10.3745/KIPSTB.2003.10B.3.297
Nowdays, with the explosive growth of the web information, web users Increase requests of systems which collect and analyze web pages that are relevant. The systems which were develop to solve the request were used clustering methods to improve the duality of information. Clustering is defining inter relationship of unordered data and grouping data systematically. The systems using clustering provide the grouped information to the users. So, they understand the information efficiently. We proposed a hybrid clustering method to cluster a large quantity of data efficiently. By that method, We generate initial clusters using COBWEB Algorithm and refine them using Ezioni Algorithm. This paper adds two ideas in prior hybrid clustering method to increment accuracy and efficiency of clusters. Firstly, we propose the clustering method considering weight of attributes of data. Second, we redefine evaluation functions which generate initial clusters to increase efficiency in clustering. Clustering method proposed in this paper processes a large quantity of data and diminish of dependancy on sequence of input of data. So the clusters are useful to make user profiles in high quality. Ultimately, we will show that the proposed clustering method outperforms the pervious clustering method in the aspect of precision and execution speed.
A High-speed Automatic Precision Inspection System for Bolts Defects
Oh, Choon-Suk ; Lee, Hyun-Min ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 305~310
DOI : 10.3745/KIPSTB.2003.10B.3.305
In this paper we deal with the system design and development of the high-speed automatic precision inspection for the defects of bolts. In order to inspect bolts continuously, we used the conveyor system. Also, this conveyor includes the servo motor and encoder to achieve accurate movement. According to encoder signal, line-scan camera captures the line-by-line image of bolts and after one frame is accumulated, various parameters are calculated and inspected by image processing algorithms. Experimental results using the developed facilities are presented to demonstrate the efficiency of the proposed equipment.
Multiple Texture Objects Extraction with Self-organizing Optimal Gabor-filter
Lee, Woo-Beom ; Kim, Wook-Hyun ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 311~320
DOI : 10.3745/KIPSTB.2003.10B.3.311
The Optimal filter yielding optimal texture feature separation is a most effective technique for extracting the texture objects from multiple textures images. But, most optimal filter design approaches are restricted to the issue of supervised problems. No full-unsupervised method is based on the recognition of texture objects in image. We propose a novel approach that uses unsupervised learning schemes for efficient texture image analysis, and the band-pass feature of Gabor-filter is used for the optimal filter design. In our approach, the self-organizing neural network for multiple texture image identification is based on block-based clustering. The optimal frequency of Gabor-filter is turned to the optimal frequency of the distinct texture in frequency domain by analyzing the spatial frequency. In order to show the performance of the designed filters, after we have attempted to build a various texture images. The texture objects extraction is achieved by using the designed Gabor-filter. Our experimental results show that the performance of the system is very successful.
Size-Variable Block Matching for Extracting Motion Information
Jang, Seok ; Kim, Bong-Keun ; Kim, Gye-Young ; Choi, Hyung-Il ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 321~328
DOI : 10.3745/KIPSTB.2003.10B.3.321
This Paper Proposes a size-variable block matching algorithm for motion vector extraction. The Proposed algorithm dynamically determines the search area and the size of a block. We exploit the constraint of small velocity changes of a block along the time to determine the origin of the search area. The range of the search area is adjusted according to the motion coherency of spatially neighboring blocks. The process of determining the sire of a block begins matching with a small block. If the matching degree is not good enough, we expand the size of a block a little bit and then repeat the matching process until our matching criterion Is satisfied. The experimental results show that the proposed algorithm can yield very accurate block motion vectors. Our algorithm outperforms other algorithms in terms of the estimated motion vectors, though our algorithm requires some computational overhead.
A Method of Adative Background Image Generation for Object Tracking
Jee, Jeong-Gyu ; Lee, Kwang-Hyoung ; Kim, Yong-Gyun ; Oh, Hae-Seok ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 329~338
DOI : 10.3745/KIPSTB.2003.10B.3.329
Object tracking in a real time image is one of Interesting subjects in computer vision and many practical application fields past couple of years. But sometimes existing systems cannot find object by recognize background noise as object. This paper proposes a method of object detection and tracking using adaptive background image in real time. To detect object which does not influenced by illumination and remove noise in background image, this system generates adaptive background image by real time background image updating. This system detects object using the difference between background image and input image from camera. After setting up MBR(minimum bounding rectangle) using the internal point of detected object, the system tracks object through this MBR. In addition, this paper evaluates the test result about performance of proposed method as compared with existing tracking algorithm.
3D Visualization of Brain MR Images by Applying Image Interpolation Using Proportional Relationship of MBRs
Song, Mi-Young ; Cho, Hyung-Je ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 339~346
DOI : 10.3745/KIPSTB.2003.10B.3.339
In this paper, we propose a new method in which interpolation images are created by using a small number of axiai T2-weighted images instead of using many sectional images for 3D visualization of brain MR images. For image Interpolation, an important part of this process, we first segment a region of interest (ROI) that we wish to apply 3D reconstruction and extract the boundaries of segmented ROIs and MBR information. After the image size of interpolation layer is determined according to the changing rate of MBR size between top slice and bottom slice of segmented ROI, we find the corresponding pixels in segmented ROI images. Then we calculate a pixel`s intensity of interpolation image by assigning to each pixel intensity weights detected by cube interpolation method. Finally, 3D reconstruction is accomplished by exploiting feature points and 3D voxels in the created interpolation images.
Collection and Extraction Algorithm of Field-Associated Terms
Lee, Sang-Kon ; Lee, Wan-Kwon ;
The KIPS Transactions:PartB, volume 10B, issue 3, 2003, Pages 347~358
DOI : 10.3745/KIPSTB.2003.10B.3.347
VSField-associated term is a single or compound word whose terms occur in any document, and which makes it possible to recognize a field of text by using common knowledge of human. For example, human recognizes the field of document such as
, a field name of text, when she encounters a word `Pitcher` or `election`, respectively We Proposes an efficient construction method of field-associated terms (FTs) for specializing field to decide a field of text. We could fix document classification scheme from well-classified document database or corpus. Considering focus field we discuss levels and stability ranks of field-associated terms. To construct a balanced FT collection, we construct a single FTs. From the collections we could automatically construct FT`s levels, and stability ranks. We propose a new extraction algorithms of FT`s for document classification by using FT`s concentration rate, its occurrence frequencies.