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Статті в журналах з теми "KNN CLASSIFIER"
Demidova, Liliya A. "Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms." Symmetry 13, no. 4 (April 7, 2021): 615. http://dx.doi.org/10.3390/sym13040615.
Повний текст джерелаHu, Juan, Hong Peng, Jun Wang, and Wenping Yu. "kNN-P: A kNN classifier optimized by P systems." Theoretical Computer Science 817 (May 2020): 55–65. http://dx.doi.org/10.1016/j.tcs.2020.01.001.
Повний текст джерелаPAO, TSANG-LONG, YUN-MAW CHENG, YU-TE CHEN, and JUN-HENG YEH. "PERFORMANCE EVALUATION OF DIFFERENT WEIGHTING SCHEMES ON KNN-BASED EMOTION RECOGNITION IN MANDARIN SPEECH." International Journal of Information Acquisition 04, no. 04 (December 2007): 339–46. http://dx.doi.org/10.1142/s021987890700140x.
Повний текст джерелаMurugan, S., Ganesh Babu T R, and Srinivasan C. "Underwater Object Recognition Using KNN Classifier." International Journal of MC Square Scientific Research 9, no. 3 (December 17, 2017): 48. http://dx.doi.org/10.20894/ijmsr.117.009.003.007.
Повний текст джерелаMohamed, Taha M. "Pulsar selection using fuzzy knn classifier." Future Computing and Informatics Journal 3, no. 1 (June 2018): 1–6. http://dx.doi.org/10.1016/j.fcij.2017.11.001.
Повний текст джерелаKhan, Asfandyar, Abdullah Khan, Muhammad Muntazir Khan, Kamran Farid, Muhammad Mansoor Alam, and Mazliham Bin Mohd Su’ud. "Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier." Diagnostics 12, no. 11 (October 26, 2022): 2595. http://dx.doi.org/10.3390/diagnostics12112595.
Повний текст джерелаWidyadhana, Arya, Cornelius Bagus Purnama Putra, Rarasmaya Indraswari, and Agus Zainal Arifin. "A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier." Jurnal Ilmu Komputer dan Informasi 14, no. 1 (February 28, 2021): 65–71. http://dx.doi.org/10.21609/jiki.v14i1.959.
Повний текст джерелаZheng, Shuai, and Chris Ding. "A group lasso based sparse KNN classifier." Pattern Recognition Letters 131 (March 2020): 227–33. http://dx.doi.org/10.1016/j.patrec.2019.12.020.
Повний текст джерелаWang, Zhiping, Junying Na, and Baoyou Zheng. "An Improved kNN Classifier for Epilepsy Diagnosis." IEEE Access 8 (2020): 100022–30. http://dx.doi.org/10.1109/access.2020.2996946.
Повний текст джерелаTaguelmimt, Redha, and Rachid Beghdad. "DS-kNN." International Journal of Information Security and Privacy 15, no. 2 (April 2021): 131–44. http://dx.doi.org/10.4018/ijisp.2021040107.
Повний текст джерелаДисертації з теми "KNN CLASSIFIER"
Mestre, Ricardo Jorge Palheira. "Improvements on the KNN classifier." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10923.
Повний текст джерелаThe object classification is an important area within the artificial intelligence and its application extends to various areas, whether or not in the branch of science. Among the other classifiers, the K-nearest neighbor (KNN) is among the most simple and accurate especially in environments where the data distribution is unknown or apparently not parameterizable. This algorithm assigns the classifying element the major class in the K nearest neighbors. According to the original algorithm, this classification implies the calculation of the distances between the classifying instance and each one of the training objects. If on the one hand, having an extensive training set is an element of importance in order to obtain a high accuracy, on the other hand, it makes the classification of each object slower due to its lazy-learning algorithm nature. Indeed, this algorithm does not provide any means of storing information about the previous calculated classifications,making the calculation of the classification of two equal instances mandatory. In a way, it may be said that this classifier does not learn. This dissertation focuses on the lazy-learning fragility and intends to propose a solution that transforms the KNNinto an eager-learning classifier. In other words, it is intended that the algorithm learns effectively with the training set, thus avoiding redundant calculations. In the context of the proposed change in the algorithm, it is important to highlight the attributes that most characterize the objects according to their discriminating power. In this framework, there will be a study regarding the implementation of these transformations on data of different types: continuous and/or categorical.
Neo, TohKoon. "A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.
Повний текст джерелаBel, Haj Ali Wafa. "Minimisation de fonctions de perte calibrée pour la classification des images." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00934062.
Повний текст джерелаMackových, Marek. "Analýza experimentálních EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241981.
Повний текст джерелаPavani, Sri-Kaushik. "Methods for face detection and adaptive face recognition." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/7567.
Повний текст джерелаL'objectiu d'aquesta tesi és sobre biometria facial, específicament en els problemes de detecció de rostres i reconeixement facial. Malgrat la intensa recerca durant els últims 20 anys, la tecnologia no és infalible, de manera que no veiem l'ús dels sistemes de reconeixement de rostres en sectors crítics com la banca. En aquesta tesi, ens centrem en tres sub-problemes en aquestes dues àrees de recerca. En primer lloc, es proposa mètodes per millorar l'equilibri entre la precisió i la velocitat del detector de cares d'última generació. En segon lloc, considerem un problema que sovint s'ignora en la literatura: disminuir el temps de formació dels detectors. Es proposen dues tècniques per a aquest fi. En tercer lloc, es presenta un estudi detallat a gran escala sobre l'auto-actualització dels sistemes de reconeixement facial en un intent de respondre si el canvi constant de l'aparença facial es pot aprendre de forma automàtica.
Marin, Rodenas Alfonso. "Comparison of Automatic Classifiers’ Performances using Word-based Feature Extraction Techniques in an E-government setting." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-32363.
Повний текст джерелаLin, Ping-Min, and 林秉旻. "A real-time fall detection system using human body contours information and kNN classifier." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/17980118041306113240.
Повний текст джерела國立交通大學
多媒體工程研究所
96
In the province of Human Computer Interaction, monitor system is an important study. As long as the situation of aging society becomes more and more serious, the care costs will increase plenty. That is the reason so many domestic and foreign scholars throw themselves into the research of elderly care monitor system in order to support the existing care system and reduce the huge expenditures of labor costs. This research used and integrated the human face detection system developed by our laboratory to get the characteristics of the human body and track that. And also used k-th Nearest Neighbor classification to classify the human postures. Then using the information of the changing rate collected by many experiments this research finally can develop a fall detection system.
BHATT, PRASHANT. "CONTENT ACCESS USING FACE BIOMETRICS." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16578.
Повний текст джерелаMANOJ, DIVI SAI. "COGNITIVE ASSESSMENT THROUGH THE ANALYSIS OF EEG SIGNALS." Thesis, 2015. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16577.
Повний текст джерелаКниги з теми "KNN CLASSIFIER"
Pathak, Sudhir, and Soudamini Hota. KNN Classifier Based Approach for Multi-Class Sentiment Analysis of Twitter Data. Independently Published, 2017.
Знайти повний текст джерелаVidales, A. Machine Learning with Matlab. Supervised Learning: Knn Classifiers, Ensemble Learning, Random Forest, Boosting and Bagging. Independently Published, 2019.
Знайти повний текст джерелаЧастини книг з теми "KNN CLASSIFIER"
Aydede, Yigit. "Nonparametric Classifier - kNN." In Machine Learning Toolbox for Social Scientists, 137–55. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003381501-10.
Повний текст джерелаShang, Wenqian, Houkuan Huang, Haibin Zhu, Yongmin Lin, Youli Qu, and Hongbin Dong. "An Adaptive Fuzzy kNN Text Classifier." In Computational Science – ICCS 2006, 216–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758532_30.
Повний текст джерелаLaw, Kwok Ho, and Lam For Kwok. "IDS False Alarm Filtering Using KNN Classifier." In Information Security Applications, 114–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31815-6_10.
Повний текст джерелаKępa, Marcin, and Julian Szymański. "Two Stage SVM and kNN Text Documents Classifier." In Lecture Notes in Computer Science, 279–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19941-2_27.
Повний текст джерелаOrczyk, Tomasz, Rafal Doroz, and Piotr Porwik. "Combined kNN Classifier for Classification of Incomplete Data." In Advances in Intelligent Systems and Computing, 21–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19738-4_3.
Повний текст джерелаLu, Ruhua, Yueqing Mo, Weiqiao Yao, and Yalan Li. "A Leaf Recognition Algorithm Based on KNN Classifier." In Lecture Notes in Electrical Engineering, 1009–15. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6901-0_104.
Повний текст джерелаChikmurge, Diptee, and R. Shriram. "Marathi Handwritten Character Recognition Using SVM and KNN Classifier." In Hybrid Intelligent Systems, 319–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_32.
Повний текст джерелаZhou, Mu, Yusuke Tanimura, and Hidemoto Nakada. "One-Shot Learning Using Triplet Network with kNN Classifier." In Advances in Intelligent Systems and Computing, 227–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39878-1_21.
Повний текст джерелаMukherjee, Rajendrani, Srestha Sadhu, and Aurghyadip Kundu. "Heart Disease Detection Using Feature Selection Based KNN Classifier." In Proceedings of Data Analytics and Management, 577–85. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6289-8_48.
Повний текст джерелаMohurle, Savita, and Manoj Devare. "A Study of KNN Classifier to Predict Water Pollution Index." In Advances in Intelligent Systems and Computing, 457–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9515-5_44.
Повний текст джерелаТези доповідей конференцій з теми "KNN CLASSIFIER"
Tsoukalas, Vassilis Th, Vassilis G. Kaburlasos, and Christos Skourlas. "A granular, parametric KNN classifier." In the 17th Panhellenic Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2491845.2491892.
Повний текст джерелаManiyath, Shima Ramesh, Ramachandra Hebbar, Akshatha K.N., Architha L.S., and S. Rama Subramoniam. "Soil Color Detection Using Knn Classifier." In 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C). IEEE, 2018. http://dx.doi.org/10.1109/icdi3c.2018.00019.
Повний текст джерелаYigit, Halil. "A weighting approach for KNN classifier." In 2013 International Conference on Electronics, Computer and Computation (ICECCO). IEEE, 2013. http://dx.doi.org/10.1109/icecco.2013.6718270.
Повний текст джерелаPichardo-Morales, Francisco D., Marco A. Acevedo-Mosqueda, and Sandra L. Gomez-Coronel. "Classification of Gunshots with KNN Classifier." In EATIS '18: Euro American Conference on Telematics and Information Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3293614.3293656.
Повний текст джерелаGuo, Xinyu. "A KNN Classifier for Face Recognition." In 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2021. http://dx.doi.org/10.1109/cisce52179.2021.9445908.
Повний текст джерелаKaur, Manbir, Chintan Thacker, Laxmi Goswami, Thamizhvani TR, Imad Saeed Abdulrahman, and A. Stanley Raj. "Alzheimer’s Disease Detection using Weighted KNN Classifier in Comparison with Medium KNN Classifier with Improved Accuracy." In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2023. http://dx.doi.org/10.1109/icacite57410.2023.10183208.
Повний текст джерелаWen, Ch J., and Y. Zh Zhan. "HMM+KNN classifier for facial expression recognition." In 2008 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2008. http://dx.doi.org/10.1109/iciea.2008.4582519.
Повний текст джерелаJyothi, R., Sujit Hiwale, and Parvati V. Bhat. "Classification of labour contractions using KNN classifier." In 2016 International Conference on Systems in Medicine and Biology (ICSMB). IEEE, 2016. http://dx.doi.org/10.1109/icsmb.2016.7915100.
Повний текст джерелаHu, Juan, Guangchun Chen, Hong Peng, Jun Wang, Xiangnian Huang, and Xiaohui Luo. "A kNN classifier optimized by P systems." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393307.
Повний текст джерелаManolakos, Elias S., and Ioannis Stamoulias. "IP-cores design for the kNN classifier." In 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5537602.
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