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Статті в журналах з теми "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, no. 5 (August 22, 2023): 1–4. http://dx.doi.org/10.54026/ctes/1040.
Повний текст джерелаSun, Tong He, and Guo Qing Yan. "Land Utilization and Classification Method Based on Remote Sensing Technology." Applied Mechanics and Materials 239-240 (December 2012): 501–6. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.501.
Повний текст джерелаAbdullah, Khalid Murad, Bahaulddin Nabhan Adday, Refed Adnan Jaleel, Iman Mohammed Burhan, Mohanad Ahmed Salih, and Musaddak Maher Abdul Zahra. "Integrating of Promising Computer Network Technology with Intelligent Supervised Machine Learning for Better Performance." Webology 19, no. 1 (January 20, 2022): 3792–99. http://dx.doi.org/10.14704/web/v19i1/web19249.
Повний текст джерелаD M, Yashaswini. "Detection of Fake Online Reviews using Semi-supervised and Supervised learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 789–96. http://dx.doi.org/10.22214/ijraset.2022.44368.
Повний текст джерелаChoi, Sungchul, Mokhammad Afifuddin, and 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.
Повний текст джерелаAli, MD Mohsin, S. Vamshi, S. Shiva, and S. Bhanu Prakash. "Virtual Assistant Using Supervised Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 3239–45. http://dx.doi.org/10.22214/ijraset.2023.54262.
Повний текст джерелаWang, Xiujuan, Siwei Cao, Kangfeng Zheng, Xu Guo, and Yutong Shi. "Supervised Character Resemble Substitution Personality Adversarial Method." Electronics 12, no. 4 (February 8, 2023): 869. http://dx.doi.org/10.3390/electronics12040869.
Повний текст джерелаWang, Hanyun. "Comparing supervised and unsupervised learning in image denoising." Applied and Computational Engineering 5, no. 1 (June 14, 2023): 284–91. http://dx.doi.org/10.54254/2755-2721/5/20230581.
Повний текст джерелаChettri, Ajanta, Amal George, Dr A. Rengarajan, and 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, no. 4 (April 30, 2022): 1973–79. http://dx.doi.org/10.22214/ijraset.2022.41687.
Повний текст джерелаChettri, Ajanta, Amal George, Dr A. Rengarajan, and 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, no. 4 (April 30, 2022): 1973–79. http://dx.doi.org/10.22214/ijraset.2022.41687.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаKola, Lokesh, and 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.
Повний текст джерелаAboushady, 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.
Повний текст джерелаRollenhagen, 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.
Повний текст джерелаI 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.
Повний текст джерелаElf, Sebastian, and 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.
Повний текст джерелаSammanfattningAtt 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/.
Повний текст джерелаPersson, 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.
Повний текст джерелаHussein, 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.
Повний текст джерелаChetry, Roshan. "Web genre classification using feature selection and semi-supervised learning." Kansas State University, 2011. http://hdl.handle.net/2097/8855.
Повний текст джерелаDepartment 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.
Книги з теми "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.
Знайти повний текст джерелаUnited 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.
Знайти повний текст джерелаUnited 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.
Знайти повний текст джерелаUnited 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.
Знайти повний текст джерелаUnited 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.
Знайти повний текст джерелаHuff, Stephen. Supervised Learning with Linear Regression: An Executive Review of Hot Technology. Independently Published, 2018.
Знайти повний текст джерелаFeierherd, Guillermo Eugenio, Patricia Pesado, and 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.
Повний текст джерелаFinochietto, Jorge Raúl, and 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.
Повний текст джерелаFeierherd, Guillermo Eugenio, Patricia Mabel Pesado, and 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.
Повний текст джерелаSimari, Guillermo, and 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.
Повний текст джерелаЧастини книг з теми "SUPERVISED TECHNOLOGY"
Bhattacharyya, Debnath, Poulami Das, Samir Kumar Bandyopadhyay, and Tai-hoon Kim. "Grayscale Image Classification Using Supervised Chromosome Clustering." In Security Technology, 64–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10847-1_9.
Повний текст джерелаHuang, Feifei, Yan Yang, Tao Li, Jinyuan Zhang, Tonny Rutayisire, and Amjad Mahmood. "Semi-supervised Hierarchical Co-clustering." In 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.
Повний текст джерелаGuo, Xiyue, and Tingting He. "Leveraging Chinese Encyclopedia for Weakly Supervised Relation Extraction." In Semantic Technology, 127–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31676-5_9.
Повний текст джерелаZhang, Luhao, Linmei Hu, and Chuan Shi. "Incorporating Instance Correlations in Distantly Supervised Relation Extraction." In Semantic Technology, 177–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41407-8_12.
Повний текст джерелаHu, Guoping, Jingjing Liu, Hang Li, Yunbo Cao, Jian-Yun Nie, and Jianfeng Gao. "A Supervised Learning Approach to Entity Search." In Information Retrieval Technology, 54–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880592_5.
Повний текст джерелаMakino, Takuya, and Tomoya Iwakura. "A Boosted Supervised Semantic Indexing for Reranking." In Information Retrieval Technology, 16–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70145-5_2.
Повний текст джерелаXu, Wei-ran, Dong-xin Liu, Jun Guo, Yi-chao Cai, and Ri-le Hu. "Supervised Dual-PLSA for Personalized SMS Filtering." In Information Retrieval Technology, 254–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04769-5_22.
Повний текст джерелаMàrquez, Lluís, Gerard Escudero, David Martínez, and German Rigau. "Supervised Corpus-Based Methods for WSD." In Text, Speech and Language Technology, 167–216. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-4809-8_7.
Повний текст джерелаLu, Wei, and Min-Yen Kan. "Supervised Categorization of JavaScriptTM Using Program Analysis Features." In Information Retrieval Technology, 160–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_13.
Повний текст джерелаLi, Haibo, Yutaka Matsuo, and Mitsuru Ishizuka. "Semantic Relation Extraction Based on Semi-supervised Learning." In Information Retrieval Technology, 270–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_26.
Повний текст джерелаТези доповідей конференцій з теми "SUPERVISED TECHNOLOGY"
Ramachandran, Akshat, and Rizwan Ahmed Ansari. "Self-Supervised Depth Enhancement." In 2022 International Conference for Advancement in Technology (ICONAT). IEEE, 2022. http://dx.doi.org/10.1109/iconat53423.2022.9726086.
Повний текст джерелаFerrer, Miguel, Marcos Faundez-Zanuy, Carlos Travieso, Joan Fabregas, and Jesus Alonso. "Evaluation of supervised vs. non supervised databases for hand geometry identification." In Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology. IEEE, 2006. http://dx.doi.org/10.1109/ccst.2006.313447.
Повний текст джерелаCho, Gyusang, and Chan-Hyun Youn. "Supervised vs. Self-supervised Pre-trained models for Hand Pose Estimation." In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2022. http://dx.doi.org/10.1109/ictc55196.2022.9953011.
Повний текст джерелаMin Kye, Seong, Joon Son Chung, and Hoirin Kim. "Supervised Attention for Speaker Recognition." In 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2021. http://dx.doi.org/10.1109/slt48900.2021.9383579.
Повний текст джерелаMarques, Filipe, Pedro Costa, Filipa Castro, and Manuel Parente. "Self-Supervised Subsea SLAM for Autonomous Operations." In Offshore Technology Conference. Offshore Technology Conference, 2019. http://dx.doi.org/10.4043/29602-ms.
Повний текст джерелаHuang, Luzhe, Hanlong Chen, Tairan Liu, and Aydogan Ozcan. "Self-supervised neural network for holographic microscopy." In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_at.2023.atu3q.4.
Повний текст джерелаJain, Swapnesh, Ruchi Patel, Shubham Gupta, and Tanu Dhoot. "FAKE NEWS DETECTION USING SUPERVISED LEARNING METHOD." In ETHICS AND INFORMATION TECHNOLOGY. VOLKSON PRESS, 2020. http://dx.doi.org/10.26480/etit.02.2020.104.108.
Повний текст джерелаDu, Weizhi, Qichen Fu, and Zhengyu Huang. "A Self-Supervised Deep Model for Focal Stacking." In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_at.2022.jth3a.10.
Повний текст джерелаO'Shea, Timothy J., Nathan West, Matthew Vondal, and T. Charles Clancy. "Semi-supervised radio signal identification." In 2017 19th International Conference on Advanced Communication Technology (ICACT). IEEE, 2017. http://dx.doi.org/10.23919/icact.2017.7890052.
Повний текст джерелаInoue, Tomoya, Yujin Nakagawa, Ryota Wada, Keisuke Miyoshi, Shungo Abe, Kouhei Kuroda, Masatoshi Nishi, Hakan Bilen, and Konda Reddy Mopuri. "Early Stuck Detection Using Supervised and Unsupervised Machine Learning Approaches." In Offshore Technology Conference Asia. OTC, 2022. http://dx.doi.org/10.4043/31376-ms.
Повний текст джерелаЗвіти організацій з теми "SUPERVISED TECHNOLOGY"
Korchin, Howard. Development of a Comprehensive Supervisor Training Program for Advanced Manufacturing Technology. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada243533.
Повний текст джерелаMahdavian, Farnaz. Germany Country Report. University of Stavanger, February 2022. http://dx.doi.org/10.31265/usps.180.
Повний текст джерела