Literatura académica sobre el tema "SUPERVISED TECHNOLOGY"
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Artículos de revistas sobre el tema "SUPERVISED TECHNOLOGY"
Zohuri, Bahman. "The Evolution of Artificial Intelligence: From Supervised to Semi-Supervised and Ultimately Unsupervised Technology Trends". Current Trends in Engineering Science (CTES) 3, n.º 5 (22 de agosto de 2023): 1–4. http://dx.doi.org/10.54026/ctes/1040.
Texto completoSun, Tong He y Guo Qing Yan. "Land Utilization and Classification Method Based on Remote Sensing Technology". Applied Mechanics and Materials 239-240 (diciembre de 2012): 501–6. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.501.
Texto completoAbdullah, Khalid Murad, Bahaulddin Nabhan Adday, Refed Adnan Jaleel, Iman Mohammed Burhan, Mohanad Ahmed Salih y Musaddak Maher Abdul Zahra. "Integrating of Promising Computer Network Technology with Intelligent Supervised Machine Learning for Better Performance". Webology 19, n.º 1 (20 de enero de 2022): 3792–99. http://dx.doi.org/10.14704/web/v19i1/web19249.
Texto completoD M, Yashaswini. "Detection of Fake Online Reviews using Semi-supervised and Supervised learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 7 (31 de julio de 2022): 789–96. http://dx.doi.org/10.22214/ijraset.2022.44368.
Texto completoChoi, Sungchul, Mokhammad Afifuddin y Wonchul Seo. "A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence". IEEE Access 10 (2022): 19284–300. http://dx.doi.org/10.1109/access.2022.3151870.
Texto completoAli, MD Mohsin, S. Vamshi, S. Shiva y S. Bhanu Prakash. "Virtual Assistant Using Supervised Learning". International Journal for Research in Applied Science and Engineering Technology 11, n.º 6 (30 de junio de 2023): 3239–45. http://dx.doi.org/10.22214/ijraset.2023.54262.
Texto completoWang, Xiujuan, Siwei Cao, Kangfeng Zheng, Xu Guo y Yutong Shi. "Supervised Character Resemble Substitution Personality Adversarial Method". Electronics 12, n.º 4 (8 de febrero de 2023): 869. http://dx.doi.org/10.3390/electronics12040869.
Texto completoWang, Hanyun. "Comparing supervised and unsupervised learning in image denoising". Applied and Computational Engineering 5, n.º 1 (14 de junio de 2023): 284–91. http://dx.doi.org/10.54254/2755-2721/5/20230581.
Texto completoChettri, Ajanta, Amal George, Dr A. Rengarajan y Feon Jaison. "Research Paper on Fake Online Reviews Detection using Semi-supervised and Supervised learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 4 (30 de abril de 2022): 1973–79. http://dx.doi.org/10.22214/ijraset.2022.41687.
Texto completoChettri, Ajanta, Amal George, Dr A. Rengarajan y Feon Jaison. "Research Paper on Fake Online Reviews Detection using Semi-supervised and Supervised learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 4 (30 de abril de 2022): 1973–79. http://dx.doi.org/10.22214/ijraset.2022.41687.
Texto completoTesis sobre el tema "SUPERVISED TECHNOLOGY"
Persson, Travis. "Semi-Supervised Learning for Predicting Biochemical Properties". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447652.
Texto completoKola, Lokesh y Vigneshwar Muriki. "A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Diabetes Prediction". Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21816.
Texto completoAboushady, Moustafa. "Semi-supervised learning with HALFADO: two case studies". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425888.
Texto completoRollenhagen, Svante. "Classification of social gestures : Recognizing waving using supervised machinelearning". Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230678.
Texto completoI den har rapporten presenteras ett försök att göra gestigenkanning av gesterna vinkning samt handklappning med hjälp av ett verktyg som kan kanna igen ett antal punkter hos den mänskliga kroppen från videodata. At- tributen som användes är den maximala kovariansen från en sinus-anpassning till vinkeldata, samt det maximala och minimala värdet av anpassningen. En stodvektormaskin (Support Vector Machine) användes for inlärningen. Resultatet var en precision på 93% ± 4% där femdelad korsvalidering användes. Begränsningarna hos de använda metoderna diskuteras sedan, vilket inkluderar: brist på support for mer an en gest i video-datan, samt brister i generalitet nar det kommer till vilka attribut som anvandes. Slutligen ges förslag på framtida utvecklingar och förbättringar.
Eggertsson, Gunnar Atli. "Classification of Seismic Body Wave Phases Using Supervised Learning". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-423977.
Texto completoElf, Sebastian y Christopher Öqvist. "Comparison of supervised machine learning models forpredicting TV-ratings". Thesis, KTH, Hälsoinformatik och logistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278054.
Texto completoSammanfattningAtt manuellt förutsäga tittarsiffor för program- och annonsplacering kan vara kostsamt och tidskrävande om de är fel. Denna rapport utvärderar olika modeller som utnyttjar övervakad maskininlärning för att se om processen för att förutsäga tittarsiffror kan automatiseras med bättre noggrannhet än den manuella processen. Resultaten visar att av de två testade övervakade modellerna för maskininlärning, Random Forest och Support Vector Regression, var Random Forest den bättre modellen. Random Forest var bättre med båda de två mätningsmetoder, genomsnittligt absolut fel och kvadratiskt medelvärde fel, som används för att jämföra modellerna. Slutsatsen är att Random Forest, utvärderad med de data och de metoderna som används, inte är tillräckligt exakt för att ersätta den manuella processen. Även om detta är fallet, kan den fortfarande potentiellt användas som en del av den manuella processen för att underlätta de anställdas arbetsbelastning.Nyckelord Maskininlärning, övervakad inlärning, tittarsiffror, Support Vector Regression, Random Forest.
Pein, Raoul Pascal. "Semi-supervised image classification based on a multi-feature image query language". Thesis, University of Huddersfield, 2010. http://eprints.hud.ac.uk/id/eprint/9244/.
Texto completoPersson, Martin. "Semantic Mapping using Virtual Sensors and Fusion of Aerial Images with Sensor Data from a Ground Vehicle". Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-2186.
Texto completoHussein, Abdul Aziz. "Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning". University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042.
Texto completoChetry, Roshan. "Web genre classification using feature selection and semi-supervised learning". Kansas State University, 2011. http://hdl.handle.net/2097/8855.
Texto completoDepartment of Computing and Information Sciences
Doina Caragea
As the web pages continuously change and their number grows exponentially, the need for genre classification of web pages also increases. One simple reason for this is given by the need to group web pages into various genre categories in order to reduce the complexities of various web tasks (e.g., search). Experts unanimously agree on the huge potential of genre classification of web pages. However, while everybody agrees that genre classification of web pages is necessary, researchers face problems in finding enough labeled data to perform supervised classification of web pages into various genres. The high cost of skilled manual labor, rapid changing nature of web and never ending growth of web pages are the main reasons for the limited amount of labeled data. On the contrary unlabeled data can be acquired relatively inexpensively in comparison to labeled data. This suggests the use of semi-supervised learning approaches for genre classification, instead of using supervised approaches. Semi-supervised learning makes use of both labeled and unlabeled data for training - typically a small amount of labeled data and a large amount of unlabeled data. Semi-supervised learning have been extensively used in text classification problems. Given the link structure of the web, for web-page classification one can use link features in addition to the content features that are used for general text classification. Hence, the feature set corresponding to web-pages can be easily divided into two views, namely content and link based feature views. Intuitively, the two feature views are conditionally independent given the genre category and have the ability to predict the class on their own. The scarcity of labeled data, availability of large amounts of unlabeled data, richer set of features as compared to the conventional text classification tasks (specifically complementary and sufficient views of features) have encouraged us to use co-training as a tool to perform semi-supervised learning. During co-training labeled examples represented using the two views are used to learn distinct classifiers, which keep improving at each iteration by sharing the most confident predictions on the unlabeled data. In this work, we classify web-pages of .eu domain consisting of 1232 labeled host and 20000 unlabeled hosts (provided by the European Archive Foundation [Benczur et al., 2010]) into six different genres, using co-training. We compare our results with the results produced by standard supervised methods. We find that co-training can be an effective and cheap alternative to costly supervised learning. This is mainly due to the two independent and complementary feature sets of web: content based features and link based features.
Libros sobre el tema "SUPERVISED TECHNOLOGY"
United States. Congress. Office of Technology Assessment., ed. The Electronic supervisor: New technology, new tensions. Washington, D.C: Congress of the U.S., Office of Technology Assessment, 1987.
Buscar texto completoUnited States. Congress. Office of Technology Assessment., ed. The Electronic supervisor: New technology, new tensions. Washington, D.C: Congress of the United States, Office of Technology Assessment, 1987.
Buscar texto completoUnited States. Congress. Office of Technology Assessment., ed. The Electronic supervisor: New technology, new tensions. Washington, D.C: Congress of the U.S., Office of Technology Assessment, 1987.
Buscar texto completoUnited States. Congress. Office of Technology Assessment., ed. The Electronic supervisor: New technology, new tensions. Washington, D.C: U.S. Dept. of Education, Office of Educational Research and Improvement, 1987.
Buscar texto completoUnited States. Congress. Office of Technology Assessment., ed. The Electronic supervisor: New technology, new tensions. Washington, D.C: Congress of the U.S., Office of Technology Assessment, 1987.
Buscar texto completoHuff, Stephen. Supervised Learning with Linear Regression: An Executive Review of Hot Technology. Independently Published, 2018.
Buscar texto completoFeierherd, Guillermo Eugenio, Patricia Pesado y Osvaldo Mario Spositto, eds. Computer Science & Technology Series. Editorial de la Universidad Nacional de La Plata (EDULP), 2015. http://dx.doi.org/10.35537/10915/48825.
Texto completoFinochietto, Jorge Raúl y Patricia Mabel Pesado, eds. Computer Science & Technology Series. Editorial de la Universidad Nacional de La Plata (EDULP), 2014. http://dx.doi.org/10.35537/10915/58553.
Texto completoFeierherd, Guillermo Eugenio, Patricia Mabel Pesado y Claudia Cecilia Russo, eds. Computer Science & Technology Series. Editorial de la Universidad Nacional de La Plata (EDULP), 2016. http://dx.doi.org/10.35537/10915/58554.
Texto completoSimari, Guillermo y Hugo Padovani, eds. Computer Science & Technology Series. Editorial de la Universidad Nacional de La Plata (EDULP), 2011. http://dx.doi.org/10.35537/10915/18411.
Texto completoCapítulos de libros sobre el tema "SUPERVISED TECHNOLOGY"
Bhattacharyya, Debnath, Poulami Das, Samir Kumar Bandyopadhyay y Tai-hoon Kim. "Grayscale Image Classification Using Supervised Chromosome Clustering". En Security Technology, 64–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10847-1_9.
Texto completoHuang, Feifei, Yan Yang, Tao Li, Jinyuan Zhang, Tonny Rutayisire y Amjad Mahmood. "Semi-supervised Hierarchical Co-clustering". En Rough Sets and Knowledge Technology, 310–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31900-6_39.
Texto completoGuo, Xiyue y Tingting He. "Leveraging Chinese Encyclopedia for Weakly Supervised Relation Extraction". En Semantic Technology, 127–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31676-5_9.
Texto completoZhang, Luhao, Linmei Hu y Chuan Shi. "Incorporating Instance Correlations in Distantly Supervised Relation Extraction". En Semantic Technology, 177–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41407-8_12.
Texto completoHu, Guoping, Jingjing Liu, Hang Li, Yunbo Cao, Jian-Yun Nie y Jianfeng Gao. "A Supervised Learning Approach to Entity Search". En Information Retrieval Technology, 54–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880592_5.
Texto completoMakino, Takuya y Tomoya Iwakura. "A Boosted Supervised Semantic Indexing for Reranking". En Information Retrieval Technology, 16–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70145-5_2.
Texto completoXu, Wei-ran, Dong-xin Liu, Jun Guo, Yi-chao Cai y Ri-le Hu. "Supervised Dual-PLSA for Personalized SMS Filtering". En Information Retrieval Technology, 254–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04769-5_22.
Texto completoMàrquez, Lluís, Gerard Escudero, David Martínez y German Rigau. "Supervised Corpus-Based Methods for WSD". En Text, Speech and Language Technology, 167–216. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-4809-8_7.
Texto completoLu, Wei y Min-Yen Kan. "Supervised Categorization of JavaScriptTM Using Program Analysis Features". En Information Retrieval Technology, 160–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_13.
Texto completoLi, Haibo, Yutaka Matsuo y Mitsuru Ishizuka. "Semantic Relation Extraction Based on Semi-supervised Learning". En Information Retrieval Technology, 270–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_26.
Texto completoActas de conferencias sobre el tema "SUPERVISED TECHNOLOGY"
Ramachandran, Akshat y Rizwan Ahmed Ansari. "Self-Supervised Depth Enhancement". En 2022 International Conference for Advancement in Technology (ICONAT). IEEE, 2022. http://dx.doi.org/10.1109/iconat53423.2022.9726086.
Texto completoFerrer, Miguel, Marcos Faundez-Zanuy, Carlos Travieso, Joan Fabregas y Jesus Alonso. "Evaluation of supervised vs. non supervised databases for hand geometry identification". En Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology. IEEE, 2006. http://dx.doi.org/10.1109/ccst.2006.313447.
Texto completoCho, Gyusang y Chan-Hyun Youn. "Supervised vs. Self-supervised Pre-trained models for Hand Pose Estimation". En 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2022. http://dx.doi.org/10.1109/ictc55196.2022.9953011.
Texto completoMin Kye, Seong, Joon Son Chung y Hoirin Kim. "Supervised Attention for Speaker Recognition". En 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2021. http://dx.doi.org/10.1109/slt48900.2021.9383579.
Texto completoMarques, Filipe, Pedro Costa, Filipa Castro y Manuel Parente. "Self-Supervised Subsea SLAM for Autonomous Operations". En Offshore Technology Conference. Offshore Technology Conference, 2019. http://dx.doi.org/10.4043/29602-ms.
Texto completoHuang, Luzhe, Hanlong Chen, Tairan Liu y Aydogan Ozcan. "Self-supervised neural network for holographic microscopy". En CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_at.2023.atu3q.4.
Texto completoJain, Swapnesh, Ruchi Patel, Shubham Gupta y Tanu Dhoot. "FAKE NEWS DETECTION USING SUPERVISED LEARNING METHOD". En ETHICS AND INFORMATION TECHNOLOGY. VOLKSON PRESS, 2020. http://dx.doi.org/10.26480/etit.02.2020.104.108.
Texto completoDu, Weizhi, Qichen Fu y Zhengyu Huang. "A Self-Supervised Deep Model for Focal Stacking". En CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_at.2022.jth3a.10.
Texto completoO'Shea, Timothy J., Nathan West, Matthew Vondal y T. Charles Clancy. "Semi-supervised radio signal identification". En 2017 19th International Conference on Advanced Communication Technology (ICACT). IEEE, 2017. http://dx.doi.org/10.23919/icact.2017.7890052.
Texto completoInoue, Tomoya, Yujin Nakagawa, Ryota Wada, Keisuke Miyoshi, Shungo Abe, Kouhei Kuroda, Masatoshi Nishi, Hakan Bilen y Konda Reddy Mopuri. "Early Stuck Detection Using Supervised and Unsupervised Machine Learning Approaches". En Offshore Technology Conference Asia. OTC, 2022. http://dx.doi.org/10.4043/31376-ms.
Texto completoInformes sobre el tema "SUPERVISED TECHNOLOGY"
Korchin, Howard. Development of a Comprehensive Supervisor Training Program for Advanced Manufacturing Technology. Fort Belvoir, VA: Defense Technical Information Center, junio de 1991. http://dx.doi.org/10.21236/ada243533.
Texto completoMahdavian, Farnaz. Germany Country Report. University of Stavanger, febrero de 2022. http://dx.doi.org/10.31265/usps.180.
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