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Статті в журналах з теми "RBF-SVM"
Indraswari, Rarasmaya, and Agus Zainal Arifin. "RBF KERNEL OPTIMIZATION METHOD WITH PARTICLE SWARM OPTIMIZATION ON SVM USING THE ANALYSIS OF INPUT DATA’S MOVEMENT." Jurnal Ilmu Komputer dan Informasi 10, no. 1 (February 28, 2017): 36. http://dx.doi.org/10.21609/jiki.v10i1.410.
Повний текст джерелаSchuhmann, Ricardo M., Andreas Rausch, and Thomas Schanze. "Parameter estimation of support vector machine with radial basis function kernel using grid search with leave-p-out cross validation for classification of motion patterns of subviral particles." Current Directions in Biomedical Engineering 7, no. 2 (October 1, 2021): 121–24. http://dx.doi.org/10.1515/cdbme-2021-2031.
Повний текст джерелаHarafani, Hani. "Forward Selection pada Support Vector Machine untuk Memprediksi Kanker Payudara." Jurnal Infortech 1, no. 2 (January 14, 2020): 131–39. http://dx.doi.org/10.31294/infortech.v1i2.7398.
Повний текст джерелаZafari, Azar, Raul Zurita-Milla, and Emma Izquierdo-Verdiguier. "Evaluating the Performance of a Random Forest Kernel for Land Cover Classification." Remote Sensing 11, no. 5 (March 8, 2019): 575. http://dx.doi.org/10.3390/rs11050575.
Повний текст джерелаEckstein, Jan, Negin Moghadasi, Hermann Körperich, Elena Weise Valdés, Vanessa Sciacca, Lech Paluszkiewicz, Wolfgang Burchert, and Misagh Piran. "A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function." Diagnostics 12, no. 11 (November 4, 2022): 2693. http://dx.doi.org/10.3390/diagnostics12112693.
Повний текст джерелаKumar, Kapil. "Comprehensive Composition to Spot Intrusions by Optimized Gaussian Kernel SVM." International Journal of Knowledge-Based Organizations 12, no. 1 (January 2022): 1–27. http://dx.doi.org/10.4018/ijkbo.291689.
Повний текст джерелаJahed Armaghani, Danial, Panagiotis G. Asteris, Behnam Askarian, Mahdi Hasanipanah, Reza Tarinejad, and Van Van Huynh. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness." Sustainability 12, no. 6 (March 12, 2020): 2229. http://dx.doi.org/10.3390/su12062229.
Повний текст джерелаMohammed, Ahmed Saud, Atheer Saleem Almawla, and Salah Sabbar Thameel. "Prediction of Monthly Evaporation Model Using Artificial Intelligent Techniques in the Western Desert of Iraq-Al-Ghadaf Valley." Mathematical Modelling of Engineering Problems 9, no. 5 (December 13, 2022): 1261–70. http://dx.doi.org/10.18280/mmep.090513.
Повний текст джерелаAmelia, Octavia Dwi, Agus M. Soleh, and Septian Rahardiantoro. "Pemodelan Support Vector Machine Data Tidak Seimbang Keberhasilan Studi Mahasiswa Magister IPB." Xplore: Journal of Statistics 2, no. 1 (June 30, 2018): 33–40. http://dx.doi.org/10.29244/xplore.v2i1.76.
Повний текст джерелаSyahrial, Syahrial, Rosmin Ilham, Zulaika F. Asikin, and St Surya Indah Nurdin. "Stunting Classification in Children's Measurement Data Using Machine Learning Models." Journal La Multiapp 3, no. 2 (March 31, 2022): 52–60. http://dx.doi.org/10.37899/journallamultiapp.v3i2.614.
Повний текст джерелаДисертації з теми "RBF-SVM"
MISHRA, OM. "HUMAN MOTION ANALYSIS." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18772.
Повний текст джерелаKohram, Mojtaba. "Experiments with Support Vector Machines and Kernels." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378112059.
Повний текст джерелаSalazar, Ruiz Enriqueta. "Desarrollo de modelos predictivos de contaminantes ambientales." Doctoral thesis, Universitat Politècnica de València, 2008. http://hdl.handle.net/10251/2504.
Повний текст джерелаSalazar Ruiz, E. (2008). Desarrollo de modelos predictivos de contaminantes ambientales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2504
Palancia
Lee, Jen-Hao, and 李仁豪. "Model Selection of the Bounded SVM Formulation Using the RBF Kernel." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/98455433223164841493.
Повний текст джерела國立臺灣大學
資訊工程學研究所
89
The support vector machine (SVM) has become one of the most promising and popular methods in machine learning. Sound theory and careful implementation make SVM efficient enough to solve moderate to large problems, and the performance has been shown to be competitive with existing methods such as neural networks and decision trees. One remaining problem on the practical use of SVM is the model selection. That is, there are several parameters to tune so that better general accuracy can be achieved. This thesis works on the case of an SVM classification formulation with only bounded constraints using the RBF kernel according to the leave-one-out (loo) rate. A simple framework is approached in the first place, in which loo rates are exactly computed through a given model space. Next, some tricks are utilized to avoid unnecessary computation. Some heuristics are also proposed for locating good areas in more efficient ways according to observations on loo rates and time distribution over the model space. The experiments show that the software developed here performs well both in terms of computational time and loo rates. And the heuristics proposed here should be helpful for other SVM model selection software.
Soares, Rui Emanuel Paixão. "Driver monitoring systems of fatigue based on eye tracking." Master's thesis, 2017. http://hdl.handle.net/1822/54750.
Повний текст джерелаNowadays, road deaths as well as the injuries and monetary losses has become a global crisis. One of the main causes of road accidents is related to driver fatigue caused by sleep deprivation or disorders, being present in about 20% of accidents. Therefore, there is a growing interest in developing equipments capable to detect driver’s drowsiness to avoid potential accidents. In order to detect driver’s drowsiness, several private and public entities from around the world have been working on different technologies. Eye tracking technologies are prominent as it allows a non-intrusive monitoring of driver state. This dissertation integrates a project, which is designed to develop new features for the car’s cockpit of future. In this master’s thesis was intended to develop an algorithm, in which through the provided variables of the eye tracking system of the company Smarteye, can detected symptoms of fatigue of the driver early enough in order to avoid accidents. The development of the algorithm involves the study of Machine Learning (ML) methods able to recognize a pattern related to drowsiness. For this purpose it was necessary to create a database so that it was possible to extract relevant information for the training of the implemented ML models. The parameters provided by eye tracker were collected from volunteers who performed tests in a simulator (DSM - Driver Simulator Mockup). During each test, the participant had to respond to the KSS questionnaire to evaluate the level of fatigue. Having the database, it was necessary to apply it in the training of the Machine Learning models for pattern recognition using the algorithms Multilayer Perceptron (MLP), Deep Belief Networks (DBN), Support Vector Machines (SVM), Radial Basis Function (RBF) and compare between them using performance evaluation methods, such as accuracy, specificity, and sensitivity. In this dissertation it was intended to answer the following questions: 1- Has machine learning the potential to measure the driver’s drowsiness using eye tracking technology? 2- What are the performance of the MLP-BP, RBF, DBN and SVM (machine learning) methods on driver’s drowsiness detection? 3- Which facial features from eye tracking are more related with drowsiness? The results in this dissertation shown that the used Machine Learning models have great performance especially the Multilayer Perceptrons classifier where the facial features PERCLOS and Eyelid Opening were fundamental parameters for the estimation of the driver’s state. These results allow to claim that the eye tracking system together with the algorithm developed are a option to detect driver fatigue, and so, the research on this field should continue so that a final product can contribute to the increase road safety and hence the prevention of driving accidents.
Nos dias de hoje, as mortes na estrada, bem como os feridos e as perdas monetárias tornaram-se numa crise global. Uma das principais causas dos acidentes de viação está relacionada com o cansaço do condutor provocado pela privação ou distúrbios de sono, estando presente em cerca de 20% dos acidentes. A fim de detetar sonolência enquanto alguém está a conduzir, várias entidades privadas e públicas de todo o mundo têm trabalhado em diferentes tecnologias. As tecnologias de eye tracking são proeminentes, uma vez que permitem monitorizar o estado do motorista de forma não intrusiva. Nesta tese de mestrado pretendeu-se desenvolver um algoritmo em que, através das variáveis fornecidas pelo sistema eye tracking da empresa Smarteye, se possa detetar sintomas de fadiga do condutor com antecedência a fim de evitar acidentes. O desenvolvimento do algoritmo passa pelo estudo de métodos de Machine Learning (ML) capazes de reconhecer um padrão relacionado com a sonolência. Para esse efeito foi necessário a criação de uma base de dados para que fosse possível extrair informação relevante para o treino dos modelos implementados de ML. Os parâmetros fornecidos pelo eye tracker foram adquiridos a partir de voluntários que realizaram testes dentro de um simulador (DSM - Driver Simulator Mockup). Tendo a base de dados, foi necessário aplicá-la no treino dos modelos de Machine Learning para o reconhecimento de padrões usando os algoritmos Multilayer Perceptron (MLP), Deep Belief Network (DBN), Support Vector Machines (SVM), Radial Basis Function (RBF) e compará-los entre eles, usando métodos reconhecidos de avaliação de performance, como a precisão, especificidade e sensibilidade. Nesta dissertação procurou-se responder às seguintes questões: 1- A área de Machine Learning tem potencial para reconhecer sonolência no condutor usando a tecnologia de eye tracking? 2- Quais são os desempenhos dos modelos MLP-BP, DBN, RBF e SVM na deteção de sonolência no condutor? 3- Quais são os parâmetros faciais provenientes do eye tracking com maior correlação com a sonolência? Os resultados desta dissertação mostraram que os modelos de Machine Learning utilizados têm grande desempenho especialmente o classificador Multilayer Perceptrons onde as características faciais PERCLOS e Eyelid Opening foram parâmetros fundamentais para a estimação do estado do condutor. Estes resultados permitem afirmar que o sistema de eye tracking juntamente com o algoritmo desenvolvido são uma opção para detetar fadiga do condutor, e portanto a investigação nesta área deve continuar de modo a que se chegue a um produto final que possa contribuir para o aumento da segurança rodoviária.
Частини книг з теми "RBF-SVM"
Jiang, Huiyan, Xiangying Liu, Lingbo Zhou, Hiroshi Fujita, and Xiangrong Zhou. "Morlet-RBF SVM Model for Medical Images Classification." In Advances in Neural Networks – ISNN 2011, 121–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21090-7_14.
Повний текст джерелаKoul, Nimrita, and Sunilkumar S. Manvi. "Cancer Classification Using Mutual Information and Regularized RBF-SVM." In Machine Learning Technologies and Applications, 327–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4046-6_32.
Повний текст джерелаEl Boujnouni, Mohamed. "Generating Artworks Using One Class SVM with RBF Kernel." In International Conference on Advanced Intelligent Systems for Sustainable Development, 308–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26384-2_27.
Повний текст джерелаLu, Zhihai, and Siyuan Lu. "Petal-Image Based Flower Classification via GLCM and RBF-SVM." In Communications in Computer and Information Science, 216–27. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1925-3_16.
Повний текст джерелаDebnath, Rameswar, and Haruhisa Takahashi. "Learning Capability: Classical RBF Network vs. SVM with Gaussian Kernel." In Developments in Applied Artificial Intelligence, 293–302. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48035-8_29.
Повний текст джерелаGowda, Shreyank N. "Fiducial Points Detection of a Face Using RBF-SVM and Adaboost Classification." In Computer Vision – ACCV 2016 Workshops, 590–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54407-6_40.
Повний текст джерелаAlbatal, Rami, and Suzanne Little. "Empirical Exploration of Extreme SVM-RBF Parameter Values for Visual Object Classification." In MultiMedia Modeling, 299–306. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04117-9_28.
Повний текст джерелаAlabi, Kayode Omotosho, Sulaiman Olaniyi Abdulsalam, Roseline Oluwaseun Ogundokun, and Micheal Olaolu Arowolo. "Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF." In Communications in Computer and Information Science, 158–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69143-1_13.
Повний текст джерелаXue, Zihan, Jing Cao, Peizhen Wang, Zihuan Yin, and Dailin Zhang. "An LDA and RBF-SVM Based Classification Method for Inertinite Macerals of Coal." In Lecture Notes in Computer Science, 155–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87358-5_13.
Повний текст джерелаLiu, Yingpei, Xiangyu Wang, Feifei Zhang, Jianxiang Gao, Yujing Su, Xuewei Zhang, and Haiping Liang. "Fault Classification of Outage Transmission Lines Based on RBF-SVM and BP Neural Networks." In Lecture Notes in Electrical Engineering, 799–805. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8052-6_101.
Повний текст джерелаТези доповідей конференцій з теми "RBF-SVM"
Emara, Wael, and Mehmed Kantardzic. "The locality of RBF-SVM for incremental learning." In 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2009. http://dx.doi.org/10.1109/cidm.2009.4938671.
Повний текст джерелаLiu, Yin, and Keshab K. Parhi. "Computing RBF Kernel for SVM Classification Using Stochastic Logic." In 2016 IEEE International Workshop on Signal Processing Systems (SiPS). IEEE, 2016. http://dx.doi.org/10.1109/sips.2016.64.
Повний текст джерелаLiang, Ruiyu, Yanqiong Ding, Xuewu Zhang, and Jiasheng Chen. "Copper Strip Surface Defects Inspection Based on SVM-RBF." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.271.
Повний текст джерелаQu, Li-ping, Hao-han Zhou, Chong-jie Liu, and Zhao Lu. "Study on Multi-RBF-SVM for Transformer Fault Diagnosis." In 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2018. http://dx.doi.org/10.1109/dcabes.2018.00056.
Повний текст джерелаLiu, Zixuan, Ziyuan Dang, and Jie Yu. "Stock Price Prediction Model Based on RBF-SVM Algorithm." In 2020 International Conference on Computer Engineering and Intelligent Control (ICCEIC). IEEE, 2020. http://dx.doi.org/10.1109/icceic51584.2020.00032.
Повний текст джерелаPatro, B. Shivalal, Pruthiraj Swain, and B. Vandana. "Macromodel development for Wind Speed Estimation Using RBF-SVM." In 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2021. http://dx.doi.org/10.1109/gucon50781.2021.9573536.
Повний текст джерелаPing, Yuan, Mao Zhizhong, and Wang Fuli. "On-line adaptation algorithm for RBF kernel based FS-SVM." In 2011 23rd Chinese Control and Decision Conference (CCDC). IEEE, 2011. http://dx.doi.org/10.1109/ccdc.2011.5968914.
Повний текст джерелаEmara, Wael, and Mehmed Kantardzic. "Local properties of RBF-SVM during training for incremental learning." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178644.
Повний текст джерелаSingh, Namrta, Swpanil Agrawal, Tanya Agarwal, and Pavan Kumar Mishra. "RBF-SVM Based Resource Allocation Scheme for 5G CRAN Networks." In 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE). IEEE, 2018. http://dx.doi.org/10.1109/icraie.2018.8710423.
Повний текст джерелаTbarki, Khaoula, Salma Ben Said, Riadh Ksantini, and Zied Lachiri. "RBF kernel based SVM classification for landmine detection and discrimination." In 2016 International Image Processing, Applications and Systems (IPAS). IEEE, 2016. http://dx.doi.org/10.1109/ipas.2016.7880146.
Повний текст джерела