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1 108/2 電機系 丘建青 教授 期刊論文 發佈 Application of Support Vector Machine (SVM) in the Sentiment Analysis of Twitter DataSet , [108-2] :Application of Support Vector Machine (SVM) in the Sentiment Analysis of Twitter DataSet期刊論文Application of Support Vector Machine (SVM) in the Sentiment Analysis of Twitter DataSetKai‐Xu Han; Wei Chien; Chien‐Ching Chiu; Yu‐Ting Chenghigh-definition multimedia interface;PCB layout;electromagnetic interference;radiation resistanceApplied Sciences 10(3), p.1-14In this paper, several aspects were studied, including the e ect of an electromagnetic interference (EMI) noise interference strategy with High Definition Multimedia Interface (HDMI) 1.4, the analysis of a test on a printed circuit board (PCB) layout, and a comparison of the near field intensity radiation distribution between an EMI with a modified HDMI layout and an original layout. In this study, the near field detection instrument of APREL EM-ISight was employed to analyze the distribution of the strength of an electromagnetic noise field. After the practical validation, we found that the PCB layout complies with the stan
2 107/2 水環系 張麗秋 教授 期刊論文 發佈 Multi-output support vector machine for regional multi-step-ahead PM2. 5 forecasting , [107-2] :Multi-output support vector machine for regional multi-step-ahead PM2. 5 forecasting期刊論文Multi-output support vector machine for regional multi-step-ahead PM2. 5 forecastingYanlai Zhou; Fi-John Chang; Li-Chiu Chang; I-Feng Kao; Yi-Shin Wang; Che-Chia KangMulti-output SVM;Multi-task learning algorithm;Multi-step-ahead forecast;PM2.5 concentrations;Taipei CityScience of the Total Environment 651(1), p.230-240Air quality deteriorates fast under urbanization in recent decades. Reliable and precise regional multi-step-ahead PM2.5 forecasts are crucial and beneficial for mitigating health risks. This work explores a novel framework (MM-SVM) that combines the Multi-output Support Vector Machine (M-SVM) and the Multi-Task Learning (MTL) algorithm for effectively increasing the accuracy of regional multi-step-ahead forecasts through tackling error accumulation and propagation that is commonly encountered in regional forecasting. The Single-output SVM (S-SVM) is implemented as a benchmark. Taipei
3 103/1 企管系 陳昆皇 助理教授 期刊論文 發佈 Diagnosis of brain metastases from lung cancer using a modified electromagnetism like mechanism algorithm , [103-1] :Diagnosis of brain metastases from lung cancer using a modified electromagnetism like mechanism algorithm期刊論文Diagnosis of brain metastases from lung cancer using a modified electromagnetism like mechanism algorithmK-H Chen; K-J Wang; A-M Adrian; K-M WangBrain metastases;Electromagnetism like mechanism;Feature selection;Lung cancer;Support vector machine;Synthetic minority over-sampling techniqueJournal of Medical Systems 40(35)Brain metastases are commonly found in patients that are diagnosed with primary malignancy on their lung. Lung cancer patients with brain metastasis tend to have a poor survivability, which is less than 6 months in median. Therefore, an early and effective detection system for such disease is needed to help prolong the patients’ survivability and improved their quality of life. A modified electromagnetism-like mechanism (EM) algorithm, MEM-SVM, is proposed by combining EM algorithm with support vector machine (SVM) as the classifier and opposite sign test (OST) a
4 103/2 資管系 梁恩輝 副教授 期刊論文 發佈 Effective semantic features for facial expressions recognition using SVM , [103-2] :Effective semantic features for facial expressions recognition using SVM期刊論文Effective semantic features for facial expressions recognition using SVMHsieh, Chen-Chiung; Hsih, Mei-Hua; Jiang, Meng-Kai; Cheng, Yun-Maw, Liang, En-HuiFace detection;Active shape model(ASM);Facial expression recognition;Facial texture;Support vector machine(SVM)Multimedia Tools and Applications, pp.1-20en_US1380-7501國外SCI;EI;是USA
5 94/2 資工系 顏淑惠 教授 期刊論文 發佈 SVM Based Watermarking Technique , [94-2] :SVM Based Watermarking Technique期刊論文SVM Based Watermarking Technique一種以SVM為基礎的浮水印技術顏淑惠; Yen, Shwu-huey, Wang, Chia-jen淡江大學資訊工程學系臺北縣:淡江大學淡江理工學刊=Tamkang journal of science and engineering 9(2), pp.141-150This paper presents a digital watermarking technique based on Support Vector Machines (SVMs). Use the nice characteristic of the SVM, which can result an optimal hyperplane for the given training samples, the imperceptibility and robustness requirements of watermarks are fulfilled and optimized. In the proposed scheme, to improve imperceptibility, the watermark is embedded by asymmetrically tuning blue channels of the central and surrounding pixels at the same time. Furthermore, to promote robustness, the embedded watermark bits will be re-modified if necessary according to classifying result of the trained SVM. Our scheme uses only 128 bits in training SVM, thus it is time efficient. Watermarks are embedded in spatial domain and extracted directly from a watermarked image without the re
6 103/1 電機系 謝景棠 教授 期刊論文 發佈 Fingerprint recognition by multi-objective optimization PSO hybrid with SVM , [103-1] :Fingerprint recognition by multi-objective optimization PSO hybrid with SVM期刊論文Fingerprint recognition by multi-objective optimization PSO hybrid with SVMHsieh, Ching-Tang; Hu, Chia-Shing淡江大學電機工程學系MOPSO-CD;SVM;fingerprint recognitionMexico: Universidad Nacional Autonoma de Mexico * Centro de Ciencias Aplicadas y Desarrollo TecnologicoJournal of Applied Research and Technology 12(6), pp.1014-1024Researchers put efforts to discover more efficient ways to classification problems for a period of time. Recent years,the support vector machine (SVM) becomes a well-popular intelligence algorithm developed for dealing this kind of problem. In this paper, we used the core idea of multi-objective optimization to transform SVM into a new form. This form of SVM could help to solve the situation: in tradition, SVM is usually a single optimization equation, and parameters for this algorithm can only be determined by user’s experience, such as penalty parameter. Therefore, our algorithm is developed t
7 97/2 機電系 楊智旭 副教授 期刊論文 發佈 Stray Example Sheltering by Loss Regularized SVM and k NN Preprocessor , [97-2] :Stray Example Sheltering by Loss Regularized SVM and k NN Preprocessor期刊論文Stray Example Sheltering by Loss Regularized SVM and k NN PreprocessorYang, Chan-yun; Hsu, Che-chang; Yang, Jr-syu淡江大學機械與機電工程學系k-nearest-neighbor preprocessor; Stray training examples; Support vector machines; Classification; Pattern recognitionNew York: Springer New York LLCNeural Processing Letters 29(1), pp.7-27This paper presents a new model developed by merging a non-parametric k-nearest-neighbor (kNN) preprocessor into an underlying support vector machine (SVM) to provide shelters for meaningful training examples, especially for stray examples scattered around their counterpart examples with different class labels. Motivated by the method of adding heavier penalty to the stray example to attain a stricter loss function for optimization, the model acts to shelter stray examples. The model consists of a filtering kNN emphasizer stage and a classical classification stage. First, the filtering kNN emphasizer st
8 101/1 電機系 謝景棠 教授 期刊論文 發佈 A Real Time Hand Gesture Recognition System Based on DFT and SVM , [101-1] :A Real Time Hand Gesture Recognition System Based on DFT and SVM期刊論文A Real Time Hand Gesture Recognition System Based on DFT and SVMChen, Wen-Her; Hsieh, Ching-Tang; Liu, Tsun-Te淡江大學電機工程學系BEA; Camshift; Hand Gesture Recognition; Support Vector Machine; SVMStafa-Zurich: Trans Tech Publications Ltd.Applied Mechanics and Materials 284-287, pp.3004-3009Vision based band gesture recognition provides a more nature and powerful means for human-computer interaction. A fast detection process of hand gesture and an effective feature extraction process are presented. The proposed a hand gesture recognition algorithm comprises four main steps. First use Cam-shift algorithm to track skin color after closing process. Second, in order to extract feature, we use BEA to extract the boundary of the hand. Third, the benefits of Fourier descriptor are invariance to the starting point of the boundary, deformation, and rotation, and therefore transform the starting point of the boundary by Fourier transform
9 101/1 電機系 謝景棠 教授 期刊論文 發佈 Physical Rehabilitation Assistant System Based on Kinect , [101-1] :Physical Rehabilitation Assistant System Based on Kinect期刊論文Physical Rehabilitation Assistant System Based on KinectLiu, Tsun-Te; Hsieh, Ching-Tang, Chung, Ruei-Chi, Wang, Yuan-Sheng淡江大學電機工程學系KINECT; Rehabilitation; Support Vector Machine; SVMStafa-Zurich: Trans Tech Publications Ltd.Applied Mechanics and Materials 284-287, pp.1686-1690In this paper, we present a physical rehabilitation assistant system based on skeleton detection with Kinect. The users do not have to install the detectors on the exercise equipment anymore. And then, they can just use the rehabilitation equipment with Kinect using the skeleton detection technique. In this study, we build a normalized three-dimensional Cartesian coordinates location of correct postures under OpenNI system. We find out 15 human skeleton joints with three dimensional coordinates and calculate the feature values, than we use support vector machine (SVM) as classifier to define the accuracy of posture. Finally, the system can judge the corr
10 94/2 日文系 堀越和男 副教授 期刊論文 發佈 SVM仮説の再検証-「は」と「が」の習得の観點から- , [94-2] :SVM仮説の再検証-「は」と「が」の習得の観點から-期刊論文SVM仮説の再検証-「は」と「が」の習得の観點から-堀越和男淡江大學日本語文學系日本 東京都:大正大學大正大學大學院研究論集=Journal of the Graduate School, Taisho University 30,頁203-216已補正完成 by 李庭瑄;tku_id: 000122496;Made available in DSpace on 2013-07-11T03:18:57Z (GMT). No. of bitstreams: 0ja0385-7816國外JPN<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/71918</url></record></links>
11 101/1 資工系 蔡憶佳 副教授 期刊論文 發佈 Simplification of Support Vector Solutions Using Artificial Bee Colony Algorithm , [101-1] :Simplification of Support Vector Solutions Using Artificial Bee Colony Algorithm期刊論文Simplification of Support Vector Solutions Using Artificial Bee Colony AlgorithmTsai, Yih-Jia; Yeh, Jih-Pin淡江大學資訊工程學系Artificial bee colony (ABC) algorithm;discriminant function;support vector machine (SVM);swarm intelligence (SI)Singapore: World Scientific Publishing Co. Pte. Ltd.International Journal of Pattern Recognition and Artificial Intelligence 26(8), 1250020(14pages)Support vector machines (SVMs) are a relatively recent machine learning technique. One of the SVM problems is that SVM is considerably slower in test phase caused by the large number of support vectors, which limits its practical use. To address this problem, we propose an artificial bee colony (ABC) algorithm to search for an optimal subset of the set of support vectors obtained through the training of the SVM, such that the original discriminant function is best approximated. Experimental results show that the proposed ABC algorith
12 101/1 體育教學組 王元聖 教授 期刊論文 發佈 Physical rehabilitation assistant based on Kinect , [101-1] :Physical rehabilitation assistant based on Kinect期刊論文Physical rehabilitation assistant based on KinectLiu, Tsun Te; Hsieh, Ching Tang; Chung, Ruei Chi; Wang, Yuan Sheng淡江大學體育事務處Kinect;Rehabilitation;SVMApplied Mechanics and Materials 284-287, pp.1686-1690In this paper, we present a physical rehabilitation assistant system based on skeleton detection with Kinect. The users do not have to install the detectors on the exercise equipment anymore. And then, they can just use the rehabilitation equipment with Kinect using the skeleton detection technique. In this study, we build a normalized three-dimensional Cartesian coordinates location of correct postures under OpenNI system. We find out 15 human skeleton joints with three dimensional coordinates and calculate the feature values, than we use support vector machine (SVM) as classifier to define the accuracy of posture. Finally, the system can judge the correct degree of user’s postures. Also, we can have the rehabilitation purpose.tku_id:
13 100/2 機電系 楊智旭 副教授 期刊論文 發佈 Safe and Smooth: Mobile Agent Trajectory Smoothing by SVM , [100-2] :Safe and Smooth: Mobile Agent Trajectory Smoothing by SVM期刊論文Safe and Smooth: Mobile Agent Trajectory Smoothing by SVMYang, Chan-Yun; Yang, Jr-Syu; Lian, Feng-Li淡江大學機械與機電工程學系Path planning; Safe; Smooth; Large margin; Support vector machinesKumamoto: I C I C InternationalInternational Journal of Innovative Computing, Information and Control 8(7)pt.B, pp.4959-4978The paper presents a model merging Voronoi tessellation with an underlying support vector machine (SVM) in order to develop path planning for guiding an autonomous vehicle safely and smoothly through a space with obstacles. Being a roadmap method for path generation, the Voronoi tessellation is employed as a preprocessor to roughly fit a connection between the initial and goal configurations. Though the Voronoi path is safe for obstacle avoidance, its disjoint linear edges are unsatisfactory when smoothness is requested. Hence, an SVM postprocessor is proposed to make the segmented path smoother. By analogue to the Gaussian pote
14 98/1 機電系 楊智旭 副教授 期刊論文 發佈 Margin calibration in SVM class-imbalanced learning , [98-1] :Margin calibration in SVM class-imbalanced learning期刊論文Margin calibration in SVM class-imbalanced learningYang, Chan-yun; Yang, Jr-syu; Wang, Jian-jun淡江大學機械與機電工程學系Margin; Cost-sensitive learning; Class-imbalanced learning; Support vector machines; ClassificationAmsterdam : Elsevier BVNeurocomputing 73(1–3), pp.397–411tku_id: ;000096034;Submitted by 智旭 楊 (096034@mail.tku.edu.tw) on 2012-11-22T08:28:51Z No. of bitstreams: 0;Made available in DSpace on 2012-11-22T08:28:52Z (GMT). No. of bitstreams: 0;20121123-補正完成by Xiaoen0925-2312國外SCI是NLD<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/79042</url></record></links>
15 96/2 日文系 堀越和男 副教授 期刊論文 發佈 「は」と「が」の習得順序再考-SVM理論の応用 , [96-2] :「は」と「が」の習得順序再考-SVM理論の応用期刊論文「は」と「が」の習得順序再考-SVM理論の応用堀越和男淡江大學日本語文學系學習順序;SVM理論;SVM值;習得順序;イーミック;エティック;Emic;Etic;Acquisition order;SVM;SVM value東吳外語學報 26,頁269-292tku_id: 000122496;Made available in DSpace on 2011-10-24T01:38:22Z (GMT). No. of bitstreams: 0jp0259-3777國內否TWN<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/71917</url></record></links>
16 97/2 機電系 楊智旭 副教授 期刊論文 發佈 Emphasize heterogeneities in SVM classification by an embedded kNN preprocessor , [97-2] :Emphasize heterogeneities in SVM classification by an embedded kNN preprocessor期刊論文Emphasize heterogeneities in SVM classification by an embedded kNN preprocessor楊智旭淡江大學機械與機電工程學系Neural Processing Letters 29tku_id: 000096034;Submitted by 曉芬 游 (139570@mail.tku.edu.tw) on 2011-10-20T13:38:30Z No. of bitstreams: 0;Made available in DSpace on 2011-10-20T13:38:31Z (GMT). No. of bitstreams: 0en<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/65303</url></record></links>
17 98/1 資訊系 林慧珍 教授 期刊論文 發佈 Face Detection using SVM-Based Classification , [98-1] :Face Detection using SVM-Based Classification期刊論文Face Detection using SVM-Based ClassificationYeh, Jih-Pin; Pai, I-Chun, Wang, Chun-Wei; Yang, Fu-Wen; Lin, Hwei-Jen淡江大學資訊工程學系face detection; skin color segmentation; RGB color space; HSV color space; support vector machine;SVMAllahabad: Pushpa Publishing HouseFar East Journal of Experimental and Theoretical Artificial Intelligence 3(2), pp.113-123This paper proposes an improved version of our previously introduced face detection system based on skin color segmentation and neural networks. The new system, using a support vector machine (SVM) based method for learning and verification, consists of several stages. First, the system searches for the regions where faces might exist by using skin color information and forms a so-called skin map. After performing noise removal and some morphological operations on the skin map, it utilizes the aspect ratio of a face to find out possible face blocks, and then eye detection is carried out within e
18 99/1 資訊系 許輝煌 教授 期刊論文 發佈 Feature Selection via Correlation Coefficient Clustering , [99-1] :Feature Selection via Correlation Coefficient Clustering期刊論文Feature Selection via Correlation Coefficient ClusteringHsu, Hui-Huang; Hsieh, Cheng-Wei淡江大學資訊工程學系Feature Selection; Clustering; Correlation Coefficient; Support Vector Machines (SVMs); Machine Learning; ClassificationOulu: Academy PublisherJournal of Software 5(12), pp.1371-1377Feature selection is a fundamental problem in machine learning and data mining. How to choose the most problem-related features from a set of collected features is essential. In this paper, a novel method using correlation coefficient clustering in removing similar/redundant features is proposed. The collected features are grouped into clusters by measuring their correlation coefficient values. The most class-dependent feature in each cluster is retained while others in the same cluster are removed. Thus, the most class-related and mutually unrelated features are identified. The proposed method was applied to two datasets: the disordered protein datase
19 97/1 資訊系 顏淑惠 教授 期刊論文 發佈 A Multimedia Watermarking Technique Based on SVMs , [97-1] :A Multimedia Watermarking Technique Based on SVMs期刊論文A Multimedia Watermarking Technique Based on SVMsWang, Chia-jen; 顏淑惠; Yen, Shwu-huey; Wang, Patrick S.淡江大學資訊工程學系Digital watermarking; Support Vector Machines (SVMs); Tolerable Position Map (TPM); video watermarking; collusion attacks; Temporal Frame Averaging (TFA); Watermark Estimation Remodulation (WER)Singapore: World Scientific PublishingInternational Journal of Pattern Recognition and Artificial Intelligence 22(8), pp.1487-1511In this paper we present an improved support vector machines (SVMs) watermarking system for still images and video sequences. By a thorough study on feature selection for training SVM, the proposed system shows significant improvements on computation efficiency and robustness to various attacks. The improved algorithm is extended to be a scene-based video watermarking technique. In a given scene, the algorithm uses the first h' frames to train an embedding SVM, and uses the trained SVM to watermark the res
20 98/2 資訊系 林慧珍 教授 期刊論文 發佈 A hybrid optimization strategy for simplifying the solutions of support vector machines , [98-2] :A hybrid optimization strategy for simplifying the solutions of support vector machines期刊論文A hybrid optimization strategy for simplifying the solutions of support vector machinesLin, Hwei-Jen; Yeh, Jih-Pin淡江大學資訊工程學系Support vector machine; Particle swarm optimization; Genetic algorithm; Optimization; Discriminant function; HyperplaneAmsterdam: Elsevier BV * North-HollandPattern Recognition Letters 31(7), pp.563-57199學年度林慧珍教師升等參考著作The main issue is to search for a subset of the support vector solutions produced by an SVM that forms a discriminant function best approximating the original one. The work is accomplished by giving a fitness (objective function) that fairly indicates how well the discriminant function formed by a set of selected vectors approximates the original one, and searching for the set of vectors having the best fitness using PSO, EGA, or a hybrid approach combining PSO and EGA. Both the defined fitness function and the adopted search technique affect the performance. O
21 98/1 資訊系 林慧珍 教授 期刊論文 發佈 Optimal reduction of solutions for support vector machines , [98-1] :Optimal reduction of solutions for support vector machines期刊論文Optimal reduction of solutions for support vector machinesLin, Hwei-Jen; Yeh, Jih-Pin淡江大學資訊工程學系Support vector machine;Vector correlation;Genetic algorithms;Optimal solution;Discriminant function;Pattern recognitionPhiladelphia: Elsevier Inc.Applied Mathematics and Computation 214(2), pp.329-33599學年度林慧珍教師升等代表著作Being a universal learning machine, a support vector machine (SVM) suffers from expensive computational cost in the test phase due to the large number of support vectors, and greatly impacts its practical use. To address this problem, we proposed an adaptive genetic algorithm to optimally reduce the solutions for an SVM by selecting vectors from the trained support vector solutions, such that the selected vectors best approximate the original discriminant function. Our method can be applied to SVMs using any general kernel. The size of the reduced set can be used adaptively based on the requirement of the tasks. As such
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