Literatura académica sobre el tema "KNN CLASSIFIER"
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Artículos de revistas sobre el tema "KNN CLASSIFIER"
Demidova, Liliya A. "Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms". Symmetry 13, n.º 4 (7 de abril de 2021): 615. http://dx.doi.org/10.3390/sym13040615.
Texto completoHu, Juan, Hong Peng, Jun Wang y Wenping Yu. "kNN-P: A kNN classifier optimized by P systems". Theoretical Computer Science 817 (mayo de 2020): 55–65. http://dx.doi.org/10.1016/j.tcs.2020.01.001.
Texto completoPAO, TSANG-LONG, YUN-MAW CHENG, YU-TE CHEN y JUN-HENG YEH. "PERFORMANCE EVALUATION OF DIFFERENT WEIGHTING SCHEMES ON KNN-BASED EMOTION RECOGNITION IN MANDARIN SPEECH". International Journal of Information Acquisition 04, n.º 04 (diciembre de 2007): 339–46. http://dx.doi.org/10.1142/s021987890700140x.
Texto completoMurugan, S., Ganesh Babu T R y Srinivasan C. "Underwater Object Recognition Using KNN Classifier". International Journal of MC Square Scientific Research 9, n.º 3 (17 de diciembre de 2017): 48. http://dx.doi.org/10.20894/ijmsr.117.009.003.007.
Texto completoMohamed, Taha M. "Pulsar selection using fuzzy knn classifier". Future Computing and Informatics Journal 3, n.º 1 (junio de 2018): 1–6. http://dx.doi.org/10.1016/j.fcij.2017.11.001.
Texto completoKhan, Asfandyar, Abdullah Khan, Muhammad Muntazir Khan, Kamran Farid, Muhammad Mansoor Alam y Mazliham Bin Mohd Su’ud. "Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier". Diagnostics 12, n.º 11 (26 de octubre de 2022): 2595. http://dx.doi.org/10.3390/diagnostics12112595.
Texto completoWidyadhana, Arya, Cornelius Bagus Purnama Putra, Rarasmaya Indraswari y Agus Zainal Arifin. "A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier". Jurnal Ilmu Komputer dan Informasi 14, n.º 1 (28 de febrero de 2021): 65–71. http://dx.doi.org/10.21609/jiki.v14i1.959.
Texto completoZheng, Shuai y Chris Ding. "A group lasso based sparse KNN classifier". Pattern Recognition Letters 131 (marzo de 2020): 227–33. http://dx.doi.org/10.1016/j.patrec.2019.12.020.
Texto completoWang, Zhiping, Junying Na y Baoyou Zheng. "An Improved kNN Classifier for Epilepsy Diagnosis". IEEE Access 8 (2020): 100022–30. http://dx.doi.org/10.1109/access.2020.2996946.
Texto completoTaguelmimt, Redha y Rachid Beghdad. "DS-kNN". International Journal of Information Security and Privacy 15, n.º 2 (abril de 2021): 131–44. http://dx.doi.org/10.4018/ijisp.2021040107.
Texto completoTesis sobre el tema "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.
Texto completoThe 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.
Texto completoBel, 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.
Texto completoMackových, 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.
Texto completoPavani, Sri-Kaushik. "Methods for face detection and adaptive face recognition". Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/7567.
Texto completoL'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.
Texto completoLin, Ping-Min y 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.
Texto completo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.
Texto completoMANOJ, DIVI SAI. "COGNITIVE ASSESSMENT THROUGH THE ANALYSIS OF EEG SIGNALS". Thesis, 2015. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16577.
Texto completoLibros sobre el tema "KNN CLASSIFIER"
Pathak, Sudhir y Soudamini Hota. KNN Classifier Based Approach for Multi-Class Sentiment Analysis of Twitter Data. Independently Published, 2017.
Buscar texto completoVidales, A. Machine Learning with Matlab. Supervised Learning: Knn Classifiers, Ensemble Learning, Random Forest, Boosting and Bagging. Independently Published, 2019.
Buscar texto completoCapítulos de libros sobre el tema "KNN CLASSIFIER"
Aydede, Yigit. "Nonparametric Classifier - kNN". En Machine Learning Toolbox for Social Scientists, 137–55. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003381501-10.
Texto completoShang, Wenqian, Houkuan Huang, Haibin Zhu, Yongmin Lin, Youli Qu y Hongbin Dong. "An Adaptive Fuzzy kNN Text Classifier". En Computational Science – ICCS 2006, 216–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758532_30.
Texto completoLaw, Kwok Ho y Lam For Kwok. "IDS False Alarm Filtering Using KNN Classifier". En Information Security Applications, 114–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31815-6_10.
Texto completoKępa, Marcin y Julian Szymański. "Two Stage SVM and kNN Text Documents Classifier". En Lecture Notes in Computer Science, 279–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19941-2_27.
Texto completoOrczyk, Tomasz, Rafal Doroz y Piotr Porwik. "Combined kNN Classifier for Classification of Incomplete Data". En 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.
Texto completoLu, Ruhua, Yueqing Mo, Weiqiao Yao y Yalan Li. "A Leaf Recognition Algorithm Based on KNN Classifier". En Lecture Notes in Electrical Engineering, 1009–15. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6901-0_104.
Texto completoChikmurge, Diptee y R. Shriram. "Marathi Handwritten Character Recognition Using SVM and KNN Classifier". En Hybrid Intelligent Systems, 319–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_32.
Texto completoZhou, Mu, Yusuke Tanimura y Hidemoto Nakada. "One-Shot Learning Using Triplet Network with kNN Classifier". En 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.
Texto completoMukherjee, Rajendrani, Srestha Sadhu y Aurghyadip Kundu. "Heart Disease Detection Using Feature Selection Based KNN Classifier". En Proceedings of Data Analytics and Management, 577–85. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6289-8_48.
Texto completoMohurle, Savita y Manoj Devare. "A Study of KNN Classifier to Predict Water Pollution Index". En Advances in Intelligent Systems and Computing, 457–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9515-5_44.
Texto completoActas de conferencias sobre el tema "KNN CLASSIFIER"
Tsoukalas, Vassilis Th, Vassilis G. Kaburlasos y Christos Skourlas. "A granular, parametric KNN classifier". En the 17th Panhellenic Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2491845.2491892.
Texto completoManiyath, Shima Ramesh, Ramachandra Hebbar, Akshatha K.N., Architha L.S. y S. Rama Subramoniam. "Soil Color Detection Using Knn Classifier". En 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C). IEEE, 2018. http://dx.doi.org/10.1109/icdi3c.2018.00019.
Texto completoYigit, Halil. "A weighting approach for KNN classifier". En 2013 International Conference on Electronics, Computer and Computation (ICECCO). IEEE, 2013. http://dx.doi.org/10.1109/icecco.2013.6718270.
Texto completoPichardo-Morales, Francisco D., Marco A. Acevedo-Mosqueda y Sandra L. Gomez-Coronel. "Classification of Gunshots with KNN Classifier". En EATIS '18: Euro American Conference on Telematics and Information Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3293614.3293656.
Texto completoGuo, Xinyu. "A KNN Classifier for Face Recognition". En 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2021. http://dx.doi.org/10.1109/cisce52179.2021.9445908.
Texto completoKaur, Manbir, Chintan Thacker, Laxmi Goswami, Thamizhvani TR, Imad Saeed Abdulrahman y A. Stanley Raj. "Alzheimer’s Disease Detection using Weighted KNN Classifier in Comparison with Medium KNN Classifier with Improved Accuracy". En 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2023. http://dx.doi.org/10.1109/icacite57410.2023.10183208.
Texto completoWen, Ch J. y Y. Zh Zhan. "HMM+KNN classifier for facial expression recognition". En 2008 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2008. http://dx.doi.org/10.1109/iciea.2008.4582519.
Texto completoJyothi, R., Sujit Hiwale y Parvati V. Bhat. "Classification of labour contractions using KNN classifier". En 2016 International Conference on Systems in Medicine and Biology (ICSMB). IEEE, 2016. http://dx.doi.org/10.1109/icsmb.2016.7915100.
Texto completoHu, Juan, Guangchun Chen, Hong Peng, Jun Wang, Xiangnian Huang y Xiaohui Luo. "A kNN classifier optimized by P systems". En 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.
Texto completoManolakos, Elias S. y Ioannis Stamoulias. "IP-cores design for the kNN classifier". En 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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