Literatura académica sobre el tema "KNN classification"
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Artículos de revistas sobre el tema "KNN classification"
Gweon, Hyukjun, Matthias Schonlau y Stefan H. Steiner. "The k conditional nearest neighbor algorithm for classification and class probability estimation". PeerJ Computer Science 5 (13 de mayo de 2019): e194. http://dx.doi.org/10.7717/peerj-cs.194.
Texto completoZhang, Shichao. "Cost-sensitive KNN classification". Neurocomputing 391 (mayo de 2020): 234–42. http://dx.doi.org/10.1016/j.neucom.2018.11.101.
Texto completoZhao, Puning y Lifeng Lai. "Efficient Classification with Adaptive KNN". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de mayo de 2021): 11007–14. http://dx.doi.org/10.1609/aaai.v35i12.17314.
Texto completoZhang, Shichao, Xuelong Li, Ming Zong, Xiaofeng Zhu y Debo Cheng. "Learning k for kNN Classification". ACM Transactions on Intelligent Systems and Technology 8, n.º 3 (22 de abril de 2017): 1–19. http://dx.doi.org/10.1145/2990508.
Texto completoKhairina, Nurul, Theofil Tri Saputra Sibarani, Rizki Muliono, Zulfikar Sembiring y Muhathir Muhathir. "Identification of Pneumonia using The K-Nearest Neighbors Method using HOG Fitur Feature Extraction". JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 5, n.º 2 (26 de enero de 2022): 562–68. http://dx.doi.org/10.31289/jite.v5i2.6216.
Texto completoRaeisi Shahraki, Hadi, Saeedeh Pourahmad y Najaf Zare. "K Important Neighbors: A Novel Approach to Binary Classification in High Dimensional Data". BioMed Research International 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7560807.
Texto completoYang, Zhida, Peng Liu y Yi Yang. "Convective/Stratiform Precipitation Classification Using Ground-Based Doppler Radar Data Based on the K-Nearest Neighbor Algorithm". Remote Sensing 11, n.º 19 (29 de septiembre de 2019): 2277. http://dx.doi.org/10.3390/rs11192277.
Texto completoGanatra, Dr Dhimant. "Improving classification accuracy :The KNN approach". International Journal of Advanced Trends in Computer Science and Engineering 9, n.º 4 (25 de agosto de 2020): 6147–50. http://dx.doi.org/10.30534/ijatcse/2020/287942020.
Texto completoSu, Yixin y Sheng-Uei Guan. "Density and Distance Based KNN Approach to Classification". International Journal of Applied Evolutionary Computation 7, n.º 2 (abril de 2016): 45–60. http://dx.doi.org/10.4018/ijaec.2016040103.
Texto completoBoyko, Nataliya I. y Mykhaylo V. Muzyka. "Methods of analysis of multimodal data to increase the accuracy of classification". Applied Aspects of Information Technology 5, n.º 2 (4 de julio de 2022): 147–60. http://dx.doi.org/10.15276/aait.05.2022.11.
Texto completoTesis sobre el tema "KNN classification"
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.
Hanson, Sarah Elizabeth. "Classification of ADHD Using Heterogeneity Classes and Attention Network Task Timing". Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83610.
Texto completoMaster of Science
Bel, 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 completoLopez, Marcano Juan L. "Classification of ADHD and non-ADHD Using AR Models and Machine Learning Algorithms". Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/73688.
Texto completoMaster of Science
Li, Sichu. "Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data". ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/913.
Texto completoDo, Cao Tri. "Apprentissage de métrique temporelle multi-modale et multi-échelle pour la classification robuste de séries temporelles par plus proches voisins". Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM028/document.
Texto completoThe definition of a metric between time series is inherent to several data analysis and mining tasks, including clustering, classification or forecasting. Time series data present naturally several characteristics, called modalities, covering their amplitude, behavior or frequential spectrum, that may be expressed with varying delays and at different temporal granularity and localization - exhibited globally or locally. Combining several modalities at multiple temporal scales to learn a holistic metric is a key challenge for many real temporal data applications. This PhD proposes a Multi-modal and Multi-scale Temporal Metric Learning (M2TML) approach for robust time series nearest neighbors classification. The solution is based on the embedding of pairs of time series into a pairwise dissimilarity space, in which a large margin optimization process is performed to learn the metric. The M2TML solution is proposed for both linear and non linear contexts, and is studied for different regularizers. A sparse and interpretable variant of the solution shows the ability of the learned temporal metric to localize accurately discriminative modalities as well as their temporal scales.A wide range of 30 public and challenging datasets, encompassing images, traces and ECG data, that are linearly or non linearly separable, are used to show the efficiency and the potential of M2TML for time series nearest neighbors classification
Villa, Medina Joe Luis. "Reliability of classification and prediction in k-nearest neighbours". Doctoral thesis, Universitat Rovira i Virgili, 2013. http://hdl.handle.net/10803/127108.
Texto completoEn aquesta tesi doctoral s'ha desenvolupat el càlcul de la fiabilitat de classificació i de la fiabilitat de predicció utilitzant el mètode dels k-veïns més propers (k-nearest neighbours, kNN) i estratègies de remostreig basades en bootstrap. S'han desenvolupat, a més, dos nous mètodes de classificació: Probabilistic Bootstrap k-Nearest Neighbours (PBkNN) i Bagged k-Nearest Neighbours (Bagged kNN), i un nou mètode de predicció, el Direct OrthogonalizationkNN (DOkNN). En tots els casos, els resultats obtinguts amb els nous mètodes han estat comparables o millors que els obtinguts utilitzant mètodes clàssics de classificació i calibratge multivariant.
Ozsakabasi, Feray. "Classification Of Forest Areas By K Nearest Neighbor Method: Case Study, Antalya". Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609548/index.pdf.
Texto completoJoseph, Katherine Amanda. "Comparison of Segment and Pixel Based Non-Parametric Classification of Land Cover in the Amazon Region of Brazil Using Multitemporal Landsat TM/ETM+ Imagery". Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/32802.
Texto completoMaster of Science
Buani, Bruna Elisa Zanchetta. "Aplicação da Lógica Fuzzy kNN e análises estatísticas para seleção de características e classificação de abelhas". Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-10012011-085835/.
Texto completoThis work presents a proposal to solve the bees classification problem by implementing an algorithm based on Geometrics Morphometrics and the Shape analysis of landmarks generated from bees wings images. The algorithm is based on the K-Nearest Neighbor (K-Nearest Neighbor) algorithm and Fuzzy Logic KNN applied to the analysis and selection of two-dimensional data points relating to landmarks. This work is part of the Architecture Reference Model for Automatic identification and Taxonomic Classification System of Stingless Bee using the Wing Morphometry. The study includes selection and ordering methods for landmarks used in the algorithm by developing a mathematical model to represent the significance order, generating the most significant mathematical landmarks as input variables for Fuzzy Logic kNN. The main objective of this work is to develop a classification system for bee species. The knowledge involved in the development of this work include an overview of feature selection, unsupervised clustering and data mining, analysis of data pre-processing, statistical approaches for estimation and prediction, study of Shape, Procrustes Analysis on data that comes from Geometric Morphometry and the modification of the k-Nearest Neighbors algorithm and the Fuzzy Logic kNN. The results show that the classification in bee samples of the same species presents a accuracy above 90%, depending on the specie in analysis. The classification done between the bees species reach accuracies of 97%.
Libros sobre el tema "KNN classification"
Chōsakai, Kanagawa-ken Shokubutsushi. Kanagawa-ken shokubutsushi 1988. Yokohama-shi: Kanagawa Kenritsu Hakubutsukan, 1988.
Buscar texto completoZhongguo tu shu guan tu shu fen lei fa bian ji wei yuan hui. Zhongguo tu shu guan tu shu fen lei fa: Qi kan fen lei fa. Beijing: Shu mu wen xian chu ban she, 1987.
Buscar texto completoKankyōka, Shimane-ken (Japan) Shizen. Kaitei Shimane reddo dēta bukku 2013: Shimane-ken no zetsumetsu no osore no aru yasei shokubutsu : Shokubutsu-hen = Shimane red data book 2013. Shimane-ken Matsue-shi: Shimane-ken Kankyō Seikatsubu Shizen Kankyōka, 2013.
Buscar texto completoKankyōka, Shimane-ken (Japan) Shizen. Kaitei Shimane reddo dēta bukku 2014: Shimane-ken no zetsumetsu no osore no aru yasei dōbutsu : Dōbutsu hen = Shimane red data book 2014. Shimane-ken Matsue-shi: Shimane-ken Kankyō Seikatsubu Shizen Kankyōka, 2014.
Buscar texto completoNavajo weaving in the late twentieth century: Kin, community, and collectors. Tucson: University of Arizona Press, 2004.
Buscar texto completoScheffler, Harold W. Australian Kin Classification. Cambridge University Press, 2011.
Buscar texto completoScheffler, Harold W. Australian Kin Classification. Cambridge University Press, 2009.
Buscar texto completoScheffler, Harold W. Australian Kin Classification (Cambridge Studies in Social and Cultural Anthropology). Cambridge University Press, 2007.
Buscar texto completoTaisetsu ni shitai Nara-ken no yasei dōshokubutsu: Nara-kenban reddo dēta bukku : 2008. Nara-shi: Nara-ken Nōrinbu Shinrin Hozenka, 2008.
Buscar texto completo"Zhongguo tu shu guan tu shu fen lei fa, qi kan fen lei biao" shi yong zhi nan. Beijing tu shu guan chu ban she, 1998.
Buscar texto completoCapítulos de libros sobre el tema "KNN classification"
Ishii, Naohiro, Tsuyoshi Murai, Takahiro Yamada y Yongguang Bao. "Classification by Weighting, Similarity and kNN". En Intelligent Data Engineering and Automated Learning – IDEAL 2006, 57–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875581_7.
Texto completoGuo, Gongde, Hui Wang, David Bell, Yaxin Bi y Kieran Greer. "KNN Model-Based Approach in Classification". En On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, 986–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39964-3_62.
Texto completoZhuang, Jiaxin, Jiabin Cai, Ruixuan Wang, Jianguo Zhang y Wei-Shi Zheng. "Deep kNN for Medical Image Classification". En Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 127–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59710-8_13.
Texto completoIshii, Naohiro, Yuichi Morioka, Hiroaki Kimura y Yongguang Bao. "Classification by Multiple Reducts-kNN with Confidence". En Intelligent Data Engineering and Automated Learning – IDEAL 2010, 94–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15381-5_12.
Texto completoAshai, Mariyam, Rhea Gautam Mukherjee, Sanjana P. Mundharikar, Vinayak Dev Kuanr y R. Harikrishnan. "Classification of Astronomical Objects using KNN Algorithm". En Smart Intelligent Computing and Applications, Volume 1, 377–87. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9669-5_34.
Texto completoCosta, Bruno G., Jean Carlos Arouche Freire, Hamilton S. Cavalcante, Marcia Homci, Adriana R. G. Castro, Raimundo Viegas, Bianchi S. Meiguins y Jefferson M. Morais. "Fault Classification on Transmission Lines Using KNN-DTW". En Computational Science and Its Applications – ICCSA 2017, 174–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62392-4_13.
Texto completoBeryl Princess, P. Joyce, Salaja Silas y Elijah Blessing Rajsingh. "Classification of Road Accidents Using SVM and KNN". En Advances in Intelligent Systems and Computing, 27–41. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3514-7_3.
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 completoBhattacharya, Gautam, Koushik Ghosh y Ananda S. Chowdhury. "kNN Classification with an Outlier Informative Distance Measure". En Lecture Notes in Computer Science, 21–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69900-4_3.
Texto completoGong, An y Yanan Liu. "Improved KNN Classification Algorithm by Dynamic Obtaining K". En Advanced Research on Electronic Commerce, Web Application, and Communication, 320–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20367-1_51.
Texto completoActas de conferencias sobre el tema "KNN classification"
Deivasikamani, Ganeshkumar, Akshay C, Ananthakrishnan T y Rohith C. Manoj. "Covid Cough Classification using KNN Classification Algorithm". En 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2022. http://dx.doi.org/10.1109/icaaic53929.2022.9793198.
Texto completoWang, Zonghu y Zhijing Liu. "Graph-based KNN text classification". En 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569866.
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 completoAnagnostou, Panagiotis, Petros Barbas, Aristidis G. Vrahatis y Sotiris K. Tasoulis. "Approximate kNN Classification for Biomedical Data". En 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378126.
Texto completoThejaswini, B. M., T. Y. Satheesha y Sathish Bhairannawar. "EEG Classification Using Modified KNN Algorithm". En 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC). IEEE, 2023. http://dx.doi.org/10.1109/icaisc58445.2023.10200104.
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 completoLi, Huijuan, He Jiang, Dongyuan Wang y Bing Han. "An Improved KNN Algorithm for Text Classification". En 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). IEEE, 2018. http://dx.doi.org/10.1109/imccc.2018.00225.
Texto completoXie, Huahua, Dong Liang, Zhaojing Zhang, Hao Jin, Chen Lu y Yi Lin. "A Novel Pre-Classification Based kNN Algorithm". En 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0182.
Texto completoSonar, Poonam, Udhav Bhosle y Chandrajit Choudhury. "Mammography classification using modified hybrid SVM-KNN". En 2017 International Conference on Signal Processing and Communication (ICSPC). IEEE, 2017. http://dx.doi.org/10.1109/cspc.2017.8305858.
Texto completoNadeem, Humaira, Imran Mujaddid Rabbani, Muhammad Aslam y Martinez Enriquez A. M. "KNN-fuzzy classification for cloud service selection". En ICFNDS'18: International Conference on Future Networks and Distributed Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3231053.3231133.
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