• Title/Summary/Keyword: SVM (support vector machine)

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An Early Warning Model for Student Status Based on Genetic Algorithm-Optimized Radial Basis Kernel Support Vector Machine

  • Hui Li;Qixuan Huang;Chao Wang
    • Journal of Information Processing Systems
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    • v.20 no.2
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    • pp.263-272
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    • 2024
  • A model based on genetic algorithm optimization, GA-SVM, is proposed to warn university students of their status. This model improves the predictive effect of support vector machines. The genetic optimization algorithm is used to train the hyperparameters and adjust the kernel parameters, kernel penalty factor C, and gamma to optimize the support vector machine model, which can rapidly achieve convergence to obtain the optimal solution. The experimental model was trained on open-source datasets and validated through comparisons with random forest, backpropagation neural network, and GA-SVM models. The test results show that the genetic algorithm-optimized radial basis kernel support vector machine model GA-SVM can obtain higher accuracy rates when used for early warning in university learning.

Analysis and Implementation of Speech/Music Classification for 3GPP2 SMV Codec Based on Support Vector Machine (SMV코덱의 음성/음악 분류 성능 향상을 위한 Support Vector Machine의 적용)

  • Kim, Sang-Kyun;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.45 no.6
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    • pp.142-147
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    • 2008
  • In this paper, we propose a novel a roach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the support vector machine (SVM). The SVM makes it possible to build on an optimal hyperplane that is separated without the error where the distance between the closest vectors and the hyperplane is maximal. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are a lied to the SVM are selected from relevant parameters of the SMV for the efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

Video Summarization Using Importance-based Fuzzy One-Class Support Vector Machine (중요도 기반 퍼지 원 클래스 서포트 벡터 머신을 이용한 비디오 요약 기술)

  • Kim, Ki-Joo;Choi, Young-Sik
    • Journal of Internet Computing and Services
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    • v.12 no.5
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    • pp.87-100
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    • 2011
  • In this paper, we address a video summarization task as generating both visually salient and semantically important video segments. In order to find salient data points, one can use the OC-SVM (One-class Support Vector Machine), which is well known for novelty detection problems. It is, however, hard to incorporate into the OC-SVM process the importance measure of data points, which is crucial for video summarization. In order to integrate the importance of each point in the OC-SVM process, we propose a fuzzy version of OC-SVM. The Importance-based Fuzzy OC-SVM weights data points according to the importance measure of the video segments and then estimates the support of a distribution of the weighted feature vectors. The estimated support vectors form the descriptive segments that best delineate the underlying video content in terms of the importance and salience of video segments. We demonstrate the performance of our algorithm on several synthesized data sets and different types of videos in order to show the efficacy of the proposed algorithm. Experimental results showed that our approach outperformed the well known traditional method.

A Performance Comparison of SVM and MLP for Multiple Defect Diagnosis of Gas Turbine Engine (가스터빈 엔진의 복합 결함 진단을 위한 SVM과 MLP의 성능 비교)

  • Park Jun-Cheol;Roh Tae-Seong;Choi Dong-Whan
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2005.11a
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    • pp.158-161
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    • 2005
  • In this study, the defect diagnosis of the gas turbine engine was tried using Support Vector Machine(SVM). It is known that SVM can find the optimal solution mathematically through classifying two groups and searching for the Hyperplane of the arbitrary nonlinear boundary. The method for the decision of the gas turbine defect quantitatively was proposed using the Multi Layer SVM for classifying two groups and it was verified that SVM was shown quicker and more reliable diagnostic results than the existing Multi Layer Perceptron(MLP).

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A Study on the Performance Enhancement of Face Detection using SVM (SVM을 이용한 얼굴 검출 성능 향상에 대한 연구)

  • Lee Chi-Ceun;Jung Sung-Tae
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.9 no.2
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    • pp.330-337
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    • 2005
  • This paper proposes a method which improves the performance of face detection by using SVM(Support Vector Machine). first, it finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine). Experimental results shows that the proposed method improve accuracy of face detection in comparison with existing method.

A Study on Image Classification using Hybrid Method (하이브리드 기법을 이용한 영상 식별 연구)

  • Park, Sang-Sung;Jung, Gwi-Im;Jang, Dong-Sik
    • 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.

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A Study on Predicting Construction Cost of School Building Projects Based on Support Vector Machine Technique at the Early Project Stage (Support Vector Machine을 이용한 교육시설 초기 공사비 예측에 관한 연구)

  • Shin, Jae-Min;Park, Hyun-Young;Shin, Yoon-Seok;Kim, Gwang-Hee
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2012.11a
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    • pp.153-154
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    • 2012
  • The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So many method and techniques have developed that can estimate construction cost using limited information available in the early stage. Among the techniques, Support Vector Machine(SVM) has received attention in various field due to its excellent capacity for self-learning and generalization performance. Therefore, the purpose of this study is to verify the applicability of cost prediction model based on SVM in school building project at the early stage. Data used in this study are 139 school building cost constructed from 2004 to 2007 in Gyeonggi-Do. And prediction error rate of 7.48% in support vector machine is obtained. So the results showed applicability of using SVM model for predicting construction cost of school building projects.

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A study on improvement of Support Vector Machine with Incremental Local Outlier Factor (Incremental Local Outlier Factor를 이용한 Support Vector Machine의 성능 개선에 관한 연구)

  • Kim, Min-Kyu;Son, Su-Il;Yoo, Suk-In
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06c
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    • pp.354-357
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    • 2011
  • Support Vector Machine (SVM)은 주어진 데이터 중에서 각 클래스를 잘 표현하는 Support Vector (SV)를 계산함으로써 새로운 데이터를 분류하는 알고리즘이다. SVM은 전체 데이터 분포를 고려하지 않기 때문에 잘못된 데이터에 의해 분류가 잘못될 가능성이 적다. 하지만, SV가 잘못되었을 경우에는 정확도가 감소하게 되는 문제점이 있다. 본 논문에서는 SV가 잘못 주어진 데이터일 가능성을 고려, 아웃라이어 검출 알고리즘인 Local Outlier Factor (LOF) 알고리즘을 이용해 주어진 데이터 중 잘못된 데이터를 제거함으로써 SVM의 분류 정확도를 높이는 알고리즘을 제안하였다. 추가적으로, Incremental LOF를 이용해 새로운 데이터 중 판단하기 어려운 데이터를 제거함으로써 SVM의 정확도를 보다 향상시켰다. 제안된 방법은 두 개의 클래스를 가진 데이터에 대해서 실험하였다.

Multiple Pedestrians Detection using Motion Information and Support Vector Machine from a Moving Camera Image (이동 카메라 영상에서 움직임 정보와 Support Vector Machine을 이용한 다수 보행자 검출)

  • Lim, Jong-Seok;Park, Hyo-Jin;Kim, Wook-Hyun
    • Journal of the Institute of Convergence Signal Processing
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    • v.12 no.4
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    • pp.250-257
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    • 2011
  • In this paper, we proposed the method detecting multiple pedestrians using motion information and SVM(Support Vector Machine) from a moving camera image. First, we detect moving pedestrians from both the difference image and the projection histogram which is compensated for the camera ego-motion using corresponding feature sets. The difference image is simple method but it is not detected motionless pedestrians. Thus, to fix up this problem, we detect motionless pedestrians using SVM The SVM works well particularly in binary classification problem such as pedestrian detection. However, it is not detected in case that the pedestrians are adjacent or they move arms and legs excessively in the image. Therefore, in this paper, we proposed the method detecting motionless and adjacent pedestrians as well as people who take excessive action in the image using motion information and SVM The experimental results on our various test video sequences demonstrated the high efficiency of our approach as it had shown an average detection ratio of 94% and False Positive of 2.8%.

A Yields Prediction in the Semiconductor Manufacturing Process Using Stepwise Support Vector Machine (SSVM(Stepwise-Support Vector Machine)을 이용한 반도체 수율 예측)

  • An, Dae-Wong;Ko, Hyo-Heon;Kim, Ji-Hyun;Baek, Jun-Geol;Kim, Sung-Shick
    • IE interfaces
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    • v.22 no.3
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    • pp.252-262
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    • 2009
  • It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used the statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM (SSVM), for detecting high and low yields. SSVM is step-by-step adjustment of parameters to be precisely the classification for actual high and low yield lot. The objective of this paper is to examine the feasibility of SVM and SSVM in the yield classification. The experimental results show that SVM and SSVM provides a promising alternative to yield classification for the field data.