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序號 學年期 教師動態
1 108/1 電機系 丘建青 教授 會議論文 發佈 The Application of Support Vector Machine (SVM) on the Sentiment Analysis of Internet Posts , [108-1] :The Application of Support Vector Machine (SVM) on the Sentiment Analysis of Internet Posts會議論文The Application of Support Vector Machine (SVM) on the Sentiment Analysis of Internet PostsK. X. Han; C. C. Chiu; W. Chienen國際無20191003~20191006否TWN2019 IEEE Eurasia Conference on IOT, Communication and EngineeringYunlin, Taiwan
2 106/1 資管系 李鴻璋 副教授 會議論文 發佈 基於RFpS的集成學習於惡意程式分類之研究 , [106-1] :基於RFpS的集成學習於惡意程式分類之研究會議論文基於RFpS的集成學習於惡意程式分類之研究李鴻璋; 趙偉傑惡意程式分類;機器學習;集成學習TANet 2017臺灣網際網路研究會論文集 pp. 72~75.在惡意程式分析這領域,雖然近幾年在機器 學習與人工智慧的挹注下有顯著的分析成果,然 而,一般機器學習的分類方法遇到大量特徵時,會 有學習時間過長以及大量消耗資源的問題。 本論文提出一個稱為 RFpS(Random Forest predicated Svm)的兩段監督式集成學習的 快速分類技術。克服以往因過多的多餘特徵訊息 所造成的模型過度配適(overfitting)以及預測雜 訊的問題。RFpS 是結合 Random Forest 特徵萃取 與 SVM 強分類的學習與預測能力,針對惡意程式 進行快速及精準的分類。驗證的結果說明,RFpS 方法與單獨只用 SVM 比較下,其平均學習塑型速 度增加約4.5倍,而預測速度增加約2.5倍,平均精 準度提昇約20%,達到98.4%。zh_TW國內無20171025~20171027趙偉傑是TWNTANet 2017臺灣網際網路研究會臺灣 臺中市 東海大學
3 103/2 資管系 蕭瑞祥 教授 會議論文 發佈 網路情感分析對於手機應用程式評價之影響的研究 , [103-2] :網路情感分析對於手機應用程式評價之影響的研究會議論文網路情感分析對於手機應用程式評價之影響的研究蕭瑞祥; 酆偉寬意見探勘;情感分析;SVM第二十六屆國際資訊管理學術研討會論文集根據Surikate 與GfK調查 (2013)顯示,85%使用者會在下載應用程式(Application, APP)前,參考在APP STORE(iOS Application Store, APP STORE)上其它用戶對該APP的評價,這些參考項目包含該APP的預覽圖、銷售價格與內文評論等,可知使用者通常會參考其它用戶的使用經驗以決定是否下載該應用程式。 根據資策會FIND調查我國智慧型行動裝置持有人數於2014年底已達1400萬以上,又依據LINE的官方統計,在我國LINE的下載次數於2014年6月中突破1700萬大關,下載次數冠居所有APP;從數據上來看,LINE在我國行動族群間之普及率早已超過九成,但該軟體在APP STORE中的用戶評分卻僅有2.4分,足見用戶評分與下載次數並無決定性的相互影響關係,且難以反應出真實的評價水準。 據此,本研究欲以探討使用者對於手機應用程式的真實評價為研究目的,期望能夠透過蒐集網路評論文本,將評論文本轉化為特徵量化數據,輔以結合人工判讀內容並加以分析後,提出一可反應APP真實評價之評分模型。 為了驗證本研究提出之評分模型效能,本研究亦將透過系統發展研究法,以支持向量機(Support Vector Machine, SVM)分類模型將網路評論文本依特徵項轉化為特徵量文本後,進行訓練與測試,再將支持向量機分類模型產出之分類結果比對人工判讀之結果,計算其準確率做為系統驗證的依據。 經由實驗結果顯示,透過將詞彙做為特徵值,評分模型的整體分類準確率將近九成,顯示本研究之APP評分模型有良好的反應能力;期望本研究之研究結果能在後續相關領域下之研究有所貢獻。zh_TW國內無20150523~20150523是TWN第二十六屆國際資訊管理學術研討會(ICIM2015)大同大學
4 95/1 資工系 顏淑惠 教授 會議論文 發佈 A Watermarking Scheme Based on SVM and Tolerable Position Map , [95-1] :A Watermarking Scheme Based on SVM and Tolerable Position Map會議論文A Watermarking Scheme Based on SVM and Tolerable Position Map顏淑惠淡江大學資訊工程學系Institute of Electrical and Electronics Engineers (IEEE)Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on (Volume:4 ), pp.3170-3175This paper presents a novel digital watermarking technique based on support vector machines (SVMs) and tolerable position map (TPM). The purpose of SVMs is two folds in this study. One is using SVM to identify tolerable positions for watermark embedding on the host image, and the other is using SVM to embed and extract watermarks. By simulating common image attacks on the host image, pixels which are invincible or vulnerable are identified and used for positive or negative samples for training an SVM. Apply this SVM can create a TPM for the host image. To embed and extract watermarks, we use a known binary sequence to train an SVM such that this SVM can be applied for embedding and extractin
5 103/2 電機系 謝景棠 教授 會議論文 發佈 3D FACE MODEL CONSTRUCTION BASED ON KINECT FOR FACE RECOGNITION , [103-2] :3D FACE MODEL CONSTRUCTION BASED ON KINECT FOR FACE RECOGNITION會議論文3D FACE MODEL CONSTRUCTION BASED ON KINECT FOR FACE RECOGNITIONHsieh, Ching-Tang;Huang, Yi;Chen, Ting-Wen;Chen, Li-Ming電機工程學系暨研究所Face Recognition, PCA, SVM, KinectAcademy of Taiwan Information Systems Research (ATISR)International Conference on Internet Studies (NETs 2015)National Taipei UniversityWe propose a simpler and faster method to recognize face. First, we use Kinect 
 to detect frontal face and get depth image information with face, then we portrayed 
 face in OpenGL to construct a three-dimensional face model based on the depth 
 information. The face model also retains texture information of the original face 
 images, and to create a complete change depth of face. It has a good result of 
 repairing the distortion in side face. We can get a set face images with different angles 
 by the method proposed, In recognition part, we use
6 99/1 土木系 李英豪 教授 會議論文 發佈 Engineer Education and Attitudes Toward Mathematics: A Comparative Study , [99-1] :Engineer Education and Attitudes Toward Mathematics: A Comparative Study會議論文Engineer Education and Attitudes Toward Mathematics: A Comparative StudyKER, H. W.; LEE, Y. H.淡江大學土木工程學系International Benchmark; Index of Students’ Positive Affect Toward Mathematics; Index of Students Valuing Mathematics; Index of Students’ SelfConfidence in Learning MathematicsResearch addressed the importance of high ability in mathematics at the secondary school for the well preparation of engineering profession. However, factors like self-confidence in mathematics learning (SCM), values on mathematics (SVM), and positive attitudes toward mathematics (PATM) received less attention. This paper utilized TIMSS 2007 data to conduct a global comparative analysis on factors influencing mathematics performance at varied International Benchmark levels. TIMSS report showed that remarkable percentages of Asian students (Chinese Taipei, Korea and Singapore) reached Advanced International Benchmark. However, the report
7 97/1 資工系 呂明達 講師 會議論文 發佈 Feature Selection for Cancer Classification on Microarray Expression Data , [97-1] :Feature Selection for Cancer Classification on Microarray Expression Data會議論文Feature Selection for Cancer Classification on Microarray Expression DataHsu, Hui-huang; Lu, Ming-da淡江大學資訊工程學系Cancer Classification;Feature Selection;Microarray; Pearson Correlation Coefficient;Support Vector MachineIEEE; International Fuzzy Systems Association; National Kaohsiung University of Applied SciencesProceedings of the Eighth International Conference on Intelligent Systems Design and Applications (ISDA'08) v.3, pp.153-158Microarray is an important tool in gene analysis research. It can help identify genes that might cause various cancers. In this paper, we use feature selection methods and the support vector machine (SVM) to search for the disease-causing genes in microarray data of three different cancers. The feature selection methods are based on Euclidian distance (ED) and Pearson correlation coefficient(PCC). We investigated the effect on prediction results by training the SVM with different num
8 97/1 資工系 王駿瑋 助理教授 會議論文 發佈 Face Detection Based on SVM Classification , [97-1] :Face Detection Based on SVM Classification會議論文Face Detection Based on SVM ClassificationLin, Hwei-Jen; Wang, Chun-Wei; Pai, I-Chun淡江大學資訊工程學系Face detection;Skin color segmentation;RGB color space;HSV color space;Support vector machine (SVM)臺北縣淡水鎮 : 淡江大學第十三屆人工智慧與應用研討會論文集=The 13th conference on artificial intelligence and applications, pp.158-164淡江大學資訊工程系; 淡江大學資訊軟體系; 淡江大學資訊通科技管理學系; 中華民國人工智慧學會This paper proposes an improved version of ourpreviously introduced face detection system based onskin color segmentation and neural networks. Thenew system, using a support vector machine (SVM)based method for learning and verification, consistsof several stages. First, the system searches for theregions where faces might exist by using skin colorinformation and forms a so-called skin map. Afterperforming noise removal and some morphologicaloperations on the skin map, it utilizes the aspect ratioof a face to find out possible face blocks, and theneye detection is carried out within each possible face
9 94/2 航太系 宛 同 副教授 會議論文 發佈 台北飛航情報區第二代航情警告避撞系統(TCAS II)之空中接近事件發生頻率與模型分析 , [94-2] :台北飛航情報區第二代航情警告避撞系統(TCAS II)之空中接近事件發生頻率與模型分析會議論文台北飛航情報區第二代航情警告避撞系統(TCAS II)之空中接近事件發生頻率與模型分析耿驊; 潘思澎; 宛同淡江大學航空太空工程學系航情警告避撞系統;空中接近;支持向量機;TCAS;Near Miss;SVM航空產業創新發展學術研討會論文集,11頁真理大學觀光學院航空服務管理系民航事業首重飛航安全,而在日益擁擠的有限空域中,除了依賴傳統的地面飛航管制服務提供必要的引導和隔離外,航情警告避撞系統(TCAS)更為航機提供了及時、準確的防撞保護指引,以有效減低和避免空中接近事件的發生。本研究針對民國92年1月至94年12月間,飛航台北飛航情報區(Taipei FIR)內裝置第二代航情警告避撞系統(TCAS II)的民航機所發生的空中接近事件進行整理與分析,除對事件內容進行探討外,並應用移動平均、自迴歸分析及支持向量機(SVM)等三種預測技術建立RA事件發生頻率預測模型,並比較模型之有效性與準確性。研究結果發現SVM模型具有最佳的數據追蹤能力。tku_id: ; 000081701;Made available in DSpace on 2014-03-07T03:41:15Z (GMT). No. of bitstreams: 0;Submitted by 數位組工讀生 (deer+std@mail.tku.edu.tw) on 2014-03-07T03:41:14Z No. of bitstreams: 0;Made available in DSpace on 2014-03-07T03:41:15Z (GMT). No. of bitstreams: 0zh_TW國內20060617~20060617TWN航空產業創新發展學術研討會臺北縣, 臺灣<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/96442</url></record></links>
10 97/1 電機系 翁慶昌 教授 會議論文 發佈 A Hybrid Prototype Construction and Feature Selection Method with Parameter Optimization for Support Vector Machine , [97-1] :A Hybrid Prototype Construction and Feature Selection Method with Parameter Optimization for Support Vector Machine會議論文A Hybrid Prototype Construction and Feature Selection Method with Parameter Optimization for Support Vector MachineWong, Ching-Chang; Leu, Chun-Liang淡江大學電機工程學系Dynamic condensed nearest neighbor;Prototype construction;Feature selection;Genetic algorithm;Support vector machineProceedings of the 2008 International Computer Symposium (ICS 2008),6頁Ministry of EducationIn this paper, an order-independent algorithm for data reduction, called the Dynamic Condensed Nearest Neighbor (DCNN) rule, is proposed to adaptively construct prototypes in training dataset and to reduce the over-fitting affect with superfluous instances for the Support Vector Machine (SVM). Furthermore, a hybrid model based on the genetic algorithm is proposed to optimize the prototype construction, feature selection, and the SVM kernel parameters setting simultaneously. Several UCI benchmark datasets are c
11 97/1 資工系 林慧珍 教授 會議論文 發佈 Face Detection Based on SVM Classification , [97-1] :Face Detection Based on SVM Classification會議論文Face Detection Based on SVM ClassificationLin, Hwei-Jen; Wang, Chun-Wei; Pai, I-Chun淡江大學資訊工程學系Face detection;Skin color segmentation;RGB color space;HSV color space;Support vector machine (SVM)臺北縣淡水鎮 : 淡江大學第十三屆人工智慧與應用研討會論文集=The 13th conference on artificial intelligence and applications, pp.158-164淡江大學資訊工程系; 淡江大學資訊軟體系; 淡江大學資訊通科技管理學系; 中華民國人工智慧學會This paper proposes an improved version of ourpreviously introduced face detection system based onskin color segmentation and neural networks. Thenew system, using a support vector machine (SVM)based method for learning and verification, consistsof several stages. First, the system searches for theregions where faces might exist by using skin colorinformation and forms a so-called skin map. Afterperforming noise removal and some morphologicaloperations on the skin map, it utilizes the aspect ratioof a face to find out possible face blocks, and theneye detection is carried out within each possible face
12 102/1 資管系 戴敏育 副教授 會議論文 發佈 A Comparative Study of Data Mining Techniques for Credit Scoring in Banking , [102-1] :A Comparative Study of Data Mining Techniques for Credit Scoring in Banking會議論文A Comparative Study of Data Mining Techniques for Credit Scoring in BankingHuang, Shih-Chen; Day, Min-Yuh淡江大學資訊管理學系Classification Method;Credit Risk Score;Data Mining;SAS Enterprise Miner;Support Vector Machine (SVM)IEEE PressProceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), pp.684-691Shih-Chen Huang and Min-Yuh Day (2013), "A Comparative Study of Data Mining Techniques for Credit Scoring in Banking", in Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), San Francisco, California, USA, August 14-16, 2013, pp. 684-691.IEEECredit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and r
13 102/1 資管系 戴敏育 副教授 會議論文 發佈 Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic Analysis , [102-1] :Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic Analysis會議論文Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic AnalysisTu, Chun; Day, Min-Yuh淡江大學資訊管理學系Textual Entailment;Semantic Features; Dependency Analysis;WordNet;Syntactic Features; Machine Learning;Support Vector Machine (SVM)IEEE PressProceedings of the 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI), pp.69-74Chun Tu and Min-Yuh Day (2013), "Chinese Textual Entailment with Wordnet Semantic and Dependency Syntactic Analysis", 2013 IEEE International Workshop on Empirical Methods for Recognizing Inference in Text (IEEE EM-RITE 2013), August 14, 2013, in Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2013), San Francisco, California, USA, August 14-16, 2013, pp. 69-74.IEEERecognizing Inference in TExt (RITE) is a task for automatically detecting entailment, paraphrase, and contradiction in texts which
14 94/2 航太系 田 豐 教授 會議論文 發佈 Face Detection and Recognition Using Support Vector Machine , [94-2] :Face Detection and Recognition Using Support Vector Machine會議論文Face Detection and Recognition Using Support Vector Machine臉部辨識-支撐向量機法Tyan, Feng; Tseng, Hung-Yuan淡江大學航空太空工程學系Flexible rotor;Finite element method;Transfer matrix method;臉部辨識;支撐向量機;臉部偵測臺北縣淡水鎮:淡江大學第五屆海峽兩岸航空太空學術研討會論文集, pp.339-346淡江大學航空太空工程學系人臉的偵測和辨識在錄影監視、個人安全及人臉影像資料庫管理中扮演重要的角色。本論文中臉部辨識的核心採用支撐向量機法(SVM)。支撐向量機法不需像幾何關係法和型態法設定許多關係條件,即可進行臉部辨。支撐向量機法在處理分類問題時,不需建立知識資料庫(如模糊理論中的規則資料庫)可將輸入資料有效分類,並獲得支撐向量(SV) 和邊界(Margin) 等資訊。拉格朗日支撐向量機(LSVM) 使用迭代法來提升計算速度。我們將眼睛和嘴有效的轉換成支撐向量機計算的格式,並分別使用拉格朗日支撐向量機計算眼睛和嘴的邊界做為辨識的依據。在本系統中,採用包含92張照片和31個不同的人CVL 臉部影像資料庫做系統實驗。;Human face detection and recognition plays an important role in application such as video surveillance, personal security and face database management. Support Vector Machines (SVM) is adopted for face recognition. SVM can handle classification problem effectively without establishing the prior knowledge database, and obtain support vector and related margin. To shorten the computing time,
15 94/1 航太系 宛 同 副教授 會議論文 發佈 台北飛航情報區第二代航情警告避撞系統(TCAS II)之空中接近事件發生頻率與模型分析 , [94-1] :台北飛航情報區第二代航情警告避撞系統(TCAS II)之空中接近事件發生頻率與模型分析會議論文台北飛航情報區第二代航情警告避撞系統(TCAS II)之空中接近事件發生頻率與模型分析耿驊; 潘思澎; 宛同淡江大學航空太空工程學系航情警告避撞系統;空中接近;支持向量機;TCAS;Near Miss;SVM航空產業創新發展學術研討會論文集,11頁民航事業首重飛航安全,而在日益擁擠的有限空域中,除了依賴傳統的地面飛航管制服務提供必要的引導和隔離外,航情警告避撞系統(TCAS)更為航機提供了及時、準確的防撞保護指引,以有效減低和避免空中接近事件的發生。本研究針對民國92年1月至94年12月間,飛航台北飛航情報區(Taipei FIR)內裝置第二代航情警告避撞系統(TCAS II)的民航機所發生的空中接近事件進行整理與分析,除對事件內容進行探討外,並應用移動平均、自迴歸分析及支持向量機(SVM)等三種預測技術建立RA事件發生頻率預測模型,並比較模型之有效性與準確性。研究結果發現SVM模型具有最佳的數據追蹤能力。tku_id: ; 000081701;Made available in DSpace on 2013-03-22T11:37:15Z (GMT). No. of bitstreams: 0;20130320格式已修正by陸桂英zh_TW兩岸20060617~20060617真理大學觀光學院航空服務管理系YTWN航空產業創新發展學術研討會臺北縣, 臺灣<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/84769</url></record></links>
16 94/2 航太系 田 豐 教授 會議論文 發佈 Capture regions of GIPN guidance laws: a least square SVM approach , [94-2] :Capture regions of GIPN guidance laws: a least square SVM approach會議論文Capture regions of GIPN guidance laws: a least square SVM approachTyan, Feng淡江大學航空太空工程學系Proceedings of the American Control Conference, 2006, pp.339-344In this paper, the expression of capture region of the general ideal proportional navigation (GIPN) missile guidance law is determined by a powerful classifier, least square support vector machine (LSSVM). To reduce the computational burden, an approximation of the Gaussian radial basis function is adopted to obtain the corresponding nonlinear feature mapping function. Through numerous numerical examples, it shows that the proposed technique is adequate for the determination of capture region. All the analysis of the relative dynamics between missile and target are performed in a line of sight (LOS) fixed natural coordinate. To have the capture region ready for LSSVM, all the state variables are transformed into the modified polar variables form. In addition, to reduc
17 94/1 機電系 楊智旭 副教授 會議論文 發佈 Associating kNN and SVM for Higher Classification Accuracy , [94-1] :Associating kNN and SVM for Higher Classification Accuracy會議論文Associating kNN and SVM for Higher Classification Accuracy楊智旭淡江大學機械與機電工程學系Computational Intelligence Chapter, Hong Kong Baptist University, Xidian University, and Guangdong U2005 International Conference on Computational Intelligence and Security, Xian, Chinatku_id: 000096034;Submitted by 曉芬 游 (139570@mail.tku.edu.tw) on 2011-10-23T13:44:02Z No. of bitstreams: 0;Made available in DSpace on 2011-10-23T13:44:02Z (GMT). No. of bitstreams: 0兩岸20050915~20050919<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/70744</url></record></links>
18 101/1 資管系 戴敏育 副教授 會議論文 發佈 A Statistical Approach with Syntactic and Semantic Features for Chinese Textual Entailment , [101-1] :A Statistical Approach with Syntactic and Semantic Features for Chinese Textual Entailment會議論文A Statistical Approach with Syntactic and Semantic Features for Chinese Textual EntailmentTu, Chun; Day, Min-yuh淡江大學資訊管理學系Textual Entailment;Semantic Features;Syntactic Features;Machine Learning;Support Vector Machine (SVM)IEEE PressProceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2012), pp.59-64IEEERecognizing Textual Entailment (RTE) is a PASCAL/TAC task in which two text fragments are processed by system to determine whether the meaning of hypothesis is entailed from another text or not. In this paper, we proposed a textual entailment system using a statistical approach that integrates syntactic and semantic techniques for Recognizing Inference in Text (RITE) using the NTCIR-9 RITE task and make a comparison between semantic and syntactic features based on their differences. We thoroughly evaluate our approach using subtasks of the NTCIR-9 RITE
19 100/2 電機系 謝景棠 教授 會議論文 發佈 Physical rehabilitation assistant system based on Kinect , [100-2] :Physical rehabilitation assistant system based on Kinect會議論文Physical rehabilitation assistant system based on KinectChing-Tang Hsieh, Ruei-Chi Chung, Yeh-Kuang Wu, Liung-Chun Chang電機工程學系暨研究所Kinect, rehabilitation, SVM.pp.336-339In this paper, we set up a physical rehabilitation assistant system based on skeleton detection with Kinect produced by Microsoft. First of all, the users do not have to install the detectors on the exercise equipment. Secondly, they pay a little extra expensive to buy the rehabilitation equipments with Kinect using 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 th
20 100/2 電機系 謝景棠 教授 會議論文 發佈 AR with Hidden Marker via Touch Interface using Kinect and its Application , [100-2] :AR with Hidden Marker via Touch Interface using Kinect and its Application會議論文AR with Hidden Marker via Touch Interface using Kinect and its ApplicationChing-Tang Hsieh, Tai-Ku Kuo, Yeh-Kuang Wu, Liung-Chun Chang電機工程學系暨研究所Kinect, SVM, AR, touch screen interface, markerless.pp. 1177-1180We develop an augmented reality (AR) environment with hidden-marker via touch interface using Kinect device and then also set up a touch painting game with the AR environment. This environment is similar to that of the touch screen interface which allows user to paint picture on a tabletop with his fingers, and it is designed with depth image information from Kinect device setting up above a tabletop. We incorporate support vector machine (SVM) to classify painted pictures which correspond to the inner data and call out its AR into the tabletop in color images information from Kinect device. Because users can utilize this similar touch interface to control AR, we achieve a markerles
21 100/2 電機系 謝景棠 教授 會議論文 發佈 A Real Time Hand Gesture Recognition System Based on DFT and SVM , [100-2] :A Real Time Hand Gesture Recognition System Based on DFT and SVM會議論文A Real Time Hand Gesture Recognition System Based on DFT and SVMHsieh, Ching-tang; Yeh, Cheng-hsiang; Hung, Kuo-ming; Chen, Ting-wen; Ke, Chin-yen淡江大學電機工程學系Camshift;BEA;SVM;Hand Gesture RecognitionAdvanced Institute of Convergence Information TechnologyInformation Science and Digital Content Technology (ICIDT), 2012 8th International Conference on, pp.490-494Vision 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 Camshift 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, deforma
22 96/2 資工系 許輝煌 教授 會議論文 發佈 Protein Disordered Region Prediction by SVM with Post-Processing , [96-2] :Protein Disordered Region Prediction by SVM with Post-Processing會議論文Protein Disordered Region Prediction by SVM with Post-ProcessingHsieh, Cheng-wei; Hsu, Hui-huang; Lu, Ming-da淡江大學資訊工程學系Post processing; Protein disordered region; Protein structure prediction; SVM; SmoothingIEEE Computer SocietyProceedings of the 2nd International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2008), pp.693-698In proteomics, a proteinpsilas function is always strongly related to its structure. But, while some parts of a protein have a fixed definite structure, such as alpha-helix, beta-sheet, or coil, other parts are not associated with well-defined conformations. Previously, these so-called disordered regions were not thought to have a specific function of their own. But, recent studies suggest that some disordered regions may have important signaling or regulatory functions. In addition, some critical diseases are strongly related to these disordered regions. Hence, predicti
23 97/1 資工系 許輝煌 教授 會議論文 發佈 Feature Selection for Cancer Classification on Microarray Expression Data , [97-1] :Feature Selection for Cancer Classification on Microarray Expression Data會議論文Feature Selection for Cancer Classification on Microarray Expression DataHsu, Hui-huang; Lu, Ming-da淡江大學資訊工程學系Cancer Classification;Feature Selection;Microarray; Pearson Correlation Coefficient;Support Vector MachineIEEE; International Fuzzy Systems Association; National Kaohsiung University of Applied SciencesProceedings of the Eighth International Conference on Intelligent Systems Design and Applications (ISDA'08) v.3, pp.153-158Microarray is an important tool in gene analysis research. It can help identify genes that might cause various cancers. In this paper, we use feature selection methods and the support vector machine (SVM) to search for the disease-causing genes in microarray data of three different cancers. The feature selection methods are based on Euclidian distance (ED) and Pearson correlation coefficient(PCC). We investigated the effect on prediction results by training the SVM with different num
24 97/1 資工系 許輝煌 教授 會議論文 發佈 Protein Crystallization Prediction with a Combined Feature Set , [97-1] :Protein Crystallization Prediction with a Combined Feature Set會議論文Protein Crystallization Prediction with a Combined Feature SetHsu, Hui-huang; Wang, Shiang-ming淡江大學資訊工程學系IEEE Communication SocietyProceedings of the 5th International Conference on Innovations in Information Technology (Innovations 2008), pp.702-706Using X-ray crystallography to determine the 3D structure of a protein is a costly and time-consuming process. One of the major reasons is that the protein needs to be purified and crystallized first, and the failure rate of protein crystallization is quite high. Thus it is desired to use a computational method to predict protein crystallizability based on the primary structure information before the whole process starts. This can dramatically lower the average cost for protein structure determination. In this paper, we investigated the feature sets used in previous research. The support vector machine (SVM) was chosen as the predictor. Different weightings are set for the pe
25 97/1 資訊系 林慧珍 教授 會議論文 發佈 Face Recognition based on SVM Classification , [97-1] :Face Recognition based on SVM Classification會議論文Face Recognition based on SVM Classification林慧珍淡江大學資訊工程學系Tamkang Universitythe 13th Conference on ArtificialIntelligence and Applications (TAAI2008), Tamsuitku_id: 000086204;Made available in DSpace on 2011-10-24T03:31:41Z (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/987654321/73122</url></record></links>
26 96/2 資訊系 林慧珍 教授 會議論文 發佈 Face Detection Based on Skin Color Segmentation and SVM Classification , [96-2] :Face Detection Based on Skin Color Segmentation and SVM Classification會議論文Face Detection Based on Skin Color Segmentation and SVM ClassificationLin, Hwei-jen; Yen, Shwu-huey; Yeh, Jih-pin; Lin, Meng-ju淡江大學資訊工程學系HSV color space;RGB color space;face detection;skin color segmentation;support vector machine (SVM)Proceedings of the 2nd International Conference on Secure System Integration and Reliability Improvement (SSIRI 2008), pp.230-231This paper proposes an improved version of our previously introduced face detection system based on skin color segmentation and neural networks. The new system uses a support vector machine (SVM) based method for verification.tku_id: 000086204; 000105390;Made available in DSpace on 2011-10-24T03:31:37Z (GMT). No. of bitstreams: 0en9780769532660國際20080714~20080717YJPNSecure System Integration and Reliability Improvement, 2008. SSIRI '08. Second International Conference onYokohama, Japan<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.t
27 96/2 資訊系 顏淑惠 教授 會議論文 發佈 Face Detection Based on Skin Color Segmentation and SVM Classification , [96-2] :Face Detection Based on Skin Color Segmentation and SVM Classification會議論文Face Detection Based on Skin Color Segmentation and SVM ClassificationLin, Hwei-jen; Yen, Shwu-huey; Yeh, Jih-pin; Lin, Meng-ju淡江大學資訊工程學系HSV color space;RGB color space;face detection;skin color segmentation;support vector machine (SVM)Proceedings of the 2nd International Conference on Secure System Integration and Reliability Improvement (SSIRI 2008), pp.230-231This paper proposes an improved version of our previously introduced face detection system based on skin color segmentation and neural networks. The new system uses a support vector machine (SVM) based method for verification.tku_id: 000086204; 000105390;Made available in DSpace on 2011-10-24T03:31:37Z (GMT). No. of bitstreams: 0en9780769532660國際20080714~20080717YJPNSecure System Integration and Reliability Improvement, 2008. SSIRI '08. Second International Conference onYokohama, Japan<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.t
28 95/2 日文系 堀越和男 副教授 會議論文 發佈 「は」と「が」の習得順序再考-SVM理論の応用 , [95-2] :「は」と「が」の習得順序再考-SVM理論の応用會議論文「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東吳大學2007年日語教學國際會議tku_id: 000122496;Made available in DSpace on 2011-10-24T02:12:07Z (GMT). No. of bitstreams: 0國際20070428~20070428<links><record><name>機構典藏連結</name><url>http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/72118</url></record></links>
29 97/1 資訊系 顏淑惠 教授 會議論文 發佈 A Scene-Based Video Watermarking Technique Using SVMs , [97-1] :A Scene-Based Video Watermarking Technique Using SVMs會議論文A Scene-Based Video Watermarking Technique Using SVMs顏淑惠; Yen, Shwu-huey; Chang, Hsiao-wei; Wang, Chia-jen; Wang, Patrick S.; Chang, Mei-chueh淡江大學資訊工程學系Proceedings of International Conference on Pattern Recognition (ICPR 2008), Florida, USAtku_id:000105390;Made available in DSpace on 2010-01-11T06:06:01Z (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/987654321/37756</url></record></links>
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