• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.744-751
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• 2003

A hybrid least square Support Vector Machine combined with First Principle(FP) knowledge is proposed. We compare hybrid least square Support Vector Machine(HLS-SVM) with early proposed models such as Hybrid Neural Network(HNN) and HNN with Extended Kalman Filter(HNN-EKF). In the training and validation stage HLS-SVM shows similar performance with HNN-EKF but better than HNN, whereas, in the testing stage, it shows three times better than HNN-EKF, hundred times better than HNN model.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.477-484
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• 2001

This paper proposes a hybrid learning algorithm combined momentum and kernel-adatron for improving the performance of regression support vector machine. The momentum is utilized for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution, and the kernel-adatron algorithm is also utilized for the capability by working in nonlinear feature spaces and the simple implementation. The proposed algorithm has been applied to the 1-dimension and 2-dimension nonlinear function regression problems. The simulation results show that the proposed algorithm has better the learning speed and performance of the regression, in comparison with those quadratic programming and kernel-adatron algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.312-317
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• 2005

Ubiquitous computing has had an effect to politics, economics, society, culture, education and so forth. For effective management of huge Ubiquitous networks environment, various computers which are connected to networks has to decide automatic optimum with intelligence. Currently in many areas, data mining has been used effectively to construct intelligent systems. We proposed a hybrid support vector machine for Ubiquitous data mining which realized intelligent Ubiquitous computing environment. Many data were collected by sensor networks in Ubiquitous computing environment. There are many noises in these data. The aim of proposed method was to eliminate noises from stream data according to sensor networks. In experiment, we verified the performance of our proposed method by simulation data for Ubiquitous sensor networks.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.79-86
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• 2006

Classification technology is essential for fast retrieval in large multi-media database. This paper proposes a combining GA(Genetic Algorithm) and SVM(Support Vector Machine) model to fast retrieval. We used color and texture as feature vectors. We improved the retrieval accuracy by using proposed model which retrieves an optimal feature vector set in extracted feature vector sets. The first performance test was executed for the performance of color, texture and the feature vector combined with color and texture. The second performance test, was executed for performance of SVM and proposed algorithm. The results of the experiment, using the feature vector combined color and texture showed a good Performance than a single feature vector and the proposed algorithm using hybrid method also showed a good performance than SVM algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.433-438
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• 2003

Recently, some studies of a web-based information recommendation technique which provides users with the most necessary information through websites like a web-based shopping mall have been conducted vigorously. In most cases of web information recommendation techniques which rely on a user profile and a specific feedback from users, they require accurate and diverse profile information of users. However, in reality, it is quite difficult to acquire this related information. This paper is aimed to suggest an information prediction technique for a web information service without depending on the users'specific feedback and profile. To achieve this goal, this study is to design and implement a Dynamic Web Information Prediction System which can recommend the most useful and necessary information to users from a large volume of web data by designing and embodying Ensemble Support Vector Machine and hybrid SOM algorithm and eliminating the scarcity problem of web log data.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1032-1035
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• 2003

The field of handwriting recognition has been researched for many years. A hybrid classifier has been proven to be able to increase the recognition rate compared with a single classifier. In this paper, we combine support vector machine (SVM) and hidden Markov model (HMM) for offline handwritten numeral recognition. To improve the performance, we extract features adapted for each classifier and propose the modified SVM decision structure. The experimental results show that the proposed method can achieve improved recognition rate for handwritten numeral recognition.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.