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Статті в журналах з теми "String similarity measure"
Revesz, Peter Z. "A Tiling Algorithm-Based String Similarity Measure." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 9 (August 10, 2021): 109–12. http://dx.doi.org/10.37394/232018.2021.9.13.
Повний текст джерелаAl-Bakry, Abbas, and Marwa Al-Rikaby. "Enhanced Levenshtein Edit Distance Method functioning as a String-to-String Similarity Measure." Iraqi Journal for Computers and Informatics 42, no. 1 (December 31, 2016): 48–54. http://dx.doi.org/10.25195/ijci.v42i1.83.
Повний текст джерелаSakunthala Prabha, K. S., C. Mahesh, and S. P. Raja. "An Enhanced Semantic Focused Web Crawler Based on Hybrid String Matching Algorithm." Cybernetics and Information Technologies 21, no. 2 (June 1, 2021): 105–20. http://dx.doi.org/10.2478/cait-2021-0022.
Повний текст джерелаRakhmawati, Nur Aini, and Miftahul Jannah. "Food Ingredients Similarity Based on Conceptual and Textual Similarity." Halal Research Journal 1, no. 2 (October 27, 2021): 87–95. http://dx.doi.org/10.12962/j22759970.v1i2.107.
Повний текст джерелаZnamenskij, Sergej Vital'evich. "Stable assessment of the quality of similarity algorithms of character strings and their normalizations." Program Systems: Theory and Applications 9, no. 4 (December 28, 2018): 561–78. http://dx.doi.org/10.25209/2079-3316-2018-9-4-561-578.
Повний текст джерелаSetiawan, Rudi. "Similarity Checking Similarity Checking of Source Code Module Using Running Karp Rabin Greedy String Tiling." Science Proceedings Series 1, no. 2 (April 24, 2019): 43–46. http://dx.doi.org/10.31580/sps.v1i2.624.
Повний текст джерелаRODRIGUEZ, WLADIMIR, MARK LAST, ABRAHAM KANDEL, and HORST BUNKE. "GEOMETRIC APPROACH TO DATA MINING." International Journal of Image and Graphics 01, no. 02 (April 2001): 363–86. http://dx.doi.org/10.1142/s0219467801000220.
Повний текст джерелаSamanta, Soumitra, Steve O’Hagan, Neil Swainston, Timothy J. Roberts, and Douglas B. Kell. "VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder." Molecules 25, no. 15 (July 29, 2020): 3446. http://dx.doi.org/10.3390/molecules25153446.
Повний текст джерелаZhu, Jin, Dayu Cheng, Weiwei Zhang, Ci Song, Jie Chen, and Tao Pei. "A New Approach to Measuring the Similarity of Indoor Semantic Trajectories." ISPRS International Journal of Geo-Information 10, no. 2 (February 20, 2021): 90. http://dx.doi.org/10.3390/ijgi10020090.
Повний текст джерелаSabarish, B. A., Karthi R., and Gireesh Kumar T. "String-Based Feature Representation for Trajectory Clustering." International Journal of Embedded and Real-Time Communication Systems 10, no. 2 (April 2019): 1–18. http://dx.doi.org/10.4018/ijertcs.2019040101.
Повний текст джерелаДисертації з теми "String similarity measure"
Тодоріко, Ольга Олексіївна. "Моделі та методи очищення та інтеграції текстових даних в інформаційних системах". Thesis, Запорізький національний університет, 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/21856.
Повний текст джерелаThe thesis for the candidate degree in technical sciences, speciality 05.13.06 – Information Technologies. – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2016. In the thesis the actual scientific and practical problem of increasing the efficiency and quality of cleaning and integration of data in information reference system and information retrieval system is solved. The improvement of information technology of cleaning and integration of data is achieved by reduction of quantity of mistakes in text information by means of use of model of an inflectional paradigm, methods of creation of a lexeme index, advanced methods of tolerant retrieval. The developed model of an inflectional paradigm includes a representation of words as an ordered collection of signatures and an approximate measure of similarity between two representations. The model differs in method of dealing with forms of words and character positions. It provides the basis for the implementation of improved methods of tolerant retrieval, cleaning and integration of datasets. The method of creation of the lexeme index which is based on the offered model of an inflectional paradigm is developed, and it allows mapping a word and all its forms to a record of the index. The method of tolerant retrieval is improved at preliminary filtration stage thanks to the developed model of an inflectional paradigm and the lexeme index. The experimental efficiency evaluation indicates high precision and 99 0,5 % recall. The information technology of cleaning and integration of data is improved using the developed models and methods. The software which on the basis of the developed models and methods carries out tolerant retrieval, cleaning and integration of data sets was developed. Theoretical and practical results of the thesis are introduced in production of document flow of an entrance committee and educational process of mathematical faculty of the State institution of higher education "Zaporizhzhya National University".
Тодоріко, Ольга Олексіївна. "Моделі та методи очищення та інтеграції текстових даних в інформаційних системах". Thesis, НТУ "ХПІ", 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/21853.
Повний текст джерелаThe thesis for the candidate degree in technical sciences, speciality 05.13.06 – Information Technologies. – National Technical University «Kharkiv Polytechnic Institute», Kharkiv, 2016. In the thesis the actual scientific and practical problem of increasing the efficiency and quality of cleaning and integration of data in information reference system and information retrieval system is solved. The improvement of information technology of cleaning and integration of data is achieved by reduction of quantity of mistakes in text information by means of use of model of an inflectional paradigm, methods of creation of a lexeme index, advanced methods of tolerant retrieval. The developed model of an inflectional paradigm includes a representation of words as an ordered collection of signatures and an approximate measure of similarity between two representations. The model differs in method of dealing with forms of words and character positions. It provides the basis for the implementation of improved methods of tolerant retrieval, cleaning and integration of datasets. The method of creation of the lexeme index which is based on the offered model of an inflectional paradigm is developed, and it allows mapping a word and all its forms to a record of the index. The method of tolerant retrieval is improved at preliminary filtration stage thanks to the developed model of an inflectional paradigm and the lexeme index. The experimental efficiency evaluation indicates high precision and 99 0,5 % recall. The information technology of cleaning and integration of data is improved using the developed models and methods. The software which on the basis of the developed models and methods carries out tolerant retrieval, cleaning and integration of data sets was developed. Theoretical and practical results of the thesis are introduced in production of document flow of an entrance committee and educational process of mathematical faculty of the State institution of higher education «Zaporizhzhya National University».
Lo, Hao-Yu, and 羅浩毓. "An Improved Similarity Measure for Image Database Based on 2D C+-string." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/02733704989122668239.
Повний текст джерела靜宜大學
資訊管理學系研究所
92
In an image database system, the spatial knowledge representation is a technique of abstraction to describe an image. The 2D string and its variants are based on this concept. One of the variants, called 2D C+-string, considers sizes of and distances between objects. This method presents three major advantages: (1) more accuracy in picture representation and reconstruction; (2) less ambiguity in similarity retrieval; (3) reasoning about relative sizes, locations, and distances for a symbolic picture is possible. However, the similarity measure based on 2D C+-string doesn’t consider the ratios about sizes of and distances between objects on x- and y-axis together. The neglect will cause distorted pictures are retrieved. In this paper, we improve the similarity measure based on 2D C+-string. The improved similarity measure modifies the original equations for taking down the variation of ratios between two symbolic pictures and furthermore proposes two new types of similarity measure for discriminating pictures more precise. By having the variation of ratios, the pictorial information retrieval is more flexible for certain demands.
Chen, Yi-Ching, and 陳怡靜. "An Improved Similarity Measure Based on a New Spatial Knowledge Representation-2D Be+-String." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/96d9b3.
Повний текст джерела靜宜大學
資訊管理學系研究所
97
The spatial knowledge representation is a technique of abstraction to describe an image. This concept is mainly based on variants of 2D string. One of the variants is called 2D Be-string which is improved from the 2D B-string. With applying “dummy objects,” the 2D Be-string can represent the pictorial spatial information intuitively and naturally without cutting mechanism and any spatial operator. Besides, 2D Be-string can simplify the retrieval progress of linear transformations, including rotation and reflection of images. However, 2D Be-string ignores the sizes and distances between objects. In consequence, the representation has deficiencies in spatial reasoning and similarity retrieval. In this paper, we propose a new spatial knowledge representation scheme called 2D Be+-string which extends the work of 2D Be-string by including relative metric information about the objects of the image into the strings. Consequently, our scheme offers the advantages of more accurate similarity retrieval and possible spatial reasoning about relative sizes and distances between objects for image databases.
Rebenich, Niko. "Counting prime polynomials and measuring complexity and similarity of information." Thesis, 2016. http://hdl.handle.net/1828/7251.
Повний текст джерелаGraduate
0544 0984 0405
nrebenich@gmail.com
Книги з теми "String similarity measure"
Horne, Cynthia M. Transitional Justice in Support of Democratization. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793328.003.0008.
Повний текст джерелаRegan, Patrick M. A Perceptual Approach to Quality Peace. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190680121.003.0003.
Повний текст джерелаЧастини книг з теми "String similarity measure"
Cristo, Marco, Pável Calado, Edleno Silva de Moura, Nivio Ziviani, and Berthier Ribeiro-Neto. "Link Information as a Similarity Measure in Web Classification." In String Processing and Information Retrieval, 43–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39984-1_4.
Повний текст джерелаNguyen, Thi Thuy Anh, and Stefan Conrad. "An Improved String Similarity Measure Based on Combining Information-Theoretic and Edit Distance Methods." In Communications in Computer and Information Science, 228–39. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25840-9_15.
Повний текст джерелаFred, Ana. "Similarity Measures and Clustering of String Patterns." In Pattern Recognition and String Matching, 155–93. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4613-0231-5_7.
Повний текст джерелаArdila, Yoan José Pinzón, Raphaël Clifford, and Manal Mohamed. "Necklace Swap Problem for Rhythmic Similarity Measures." In String Processing and Information Retrieval, 234–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11575832_27.
Повний текст джерелаLuján-Mora, Sergio, and Manuel Palomar. "Comparing String Similarity Measures for Reducing Inconsistency in Integrating Data from Different Sources." In Advances in Web-Age Information Management, 191–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47714-4_18.
Повний текст джерелаAlaran, Misturah Adunni, AbdulAkeem Adesina Agboola, Adio Taofiki Akinwale, and Olusegun Folorunso. "A New LCS-Neutrosophic Similarity Measure for Text Information Retrieval." In Neutrosophic Sets in Decision Analysis and Operations Research, 258–80. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2555-5.ch012.
Повний текст джерелаHirschberg, D. S. "Serial Computations of Levenshtein Distances." In Pattern Matching Algorithms. Oxford University Press, 1997. http://dx.doi.org/10.1093/oso/9780195113679.003.0007.
Повний текст джерелаPinaire, Jessica, Etienne Chabert, Jérôme Azé, Sandra Bringay, Pascal Poncelet, and Paul Landais. "Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210167.
Повний текст джерелаShahri, Hamid Haidarian. "A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses." In Database Technologies, 2245–60. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-058-5.ch136.
Повний текст джерелаCohn, Margit. "The Nature and Use of Unilateral Executive Measures." In A Theory of the Executive Branch, 137–64. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198821984.003.0006.
Повний текст джерелаТези доповідей конференцій з теми "String similarity measure"
Debbarma, Abhijit, BS Purkayastha, and Paritosh Bhattacharya. "Stemmer for resource scarce language using string similarity measure." In 2014 International Conference on Optimization, Reliabilty, and Information Technology (ICROIT). IEEE, 2014. http://dx.doi.org/10.1109/icroit.2014.6798299.
Повний текст джерелаLu, Jiaheng, Chunbin Lin, Wei Wang, Chen Li, and Haiyong Wang. "String similarity measures and joins with synonyms." In the 2013 international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2463676.2465313.
Повний текст джерелаBilenko, Mikhail, and Raymond J. Mooney. "Adaptive duplicate detection using learnable string similarity measures." In the ninth ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/956750.956759.
Повний текст джерелаChernyak, Ekaterina. "Comparison of String Similarity Measures for Obscenity Filtering." In Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-1415.
Повний текст джерелаUnknown. "A Comparison of String Similarity Measures for Toponym Matching." In The First ACM SIGSPATIAL International Workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2534848.2534850.
Повний текст джерелаMalakasiotis, Prodromos, and Ion Androutsopoulos. "Learning textual entailment using SVMs and string similarity measures." In the ACL-PASCAL Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2007. http://dx.doi.org/10.3115/1654536.1654547.
Повний текст джерелаMontalvo, Soto, Eduardo G. Pardo, Raquel Martinez, and Victor Fresno. "Automatic cognate identification based on a fuzzy combination of string similarity measures." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6250802.
Повний текст джерелаPark, Jungseo, Seunghwan Mun, Chungmin Hyun, Byungkwon Kang, and Kwanghee Ko. "Similarity Assessment Method for Automated Curved Plate Forming." In SNAME 5th World Maritime Technology Conference. SNAME, 2015. http://dx.doi.org/10.5957/wmtc-2015-240.
Повний текст джерелаBaldwin, Timothy, Huizhi Liang, Bahar Salehi, Doris Hoogeveen, Yitong Li, and Long Duong. "UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures." In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/s16-1131.
Повний текст джерелаXu, Xinyi, Huanhuan Cao, Yanhua Yang, Erkun Yang, and Cheng Deng. "Zero-shot Metric Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/555.
Повний текст джерелаЗвіти організацій з теми "String similarity measure"
Brenan, J. M., K. Woods, J. E. Mungall, and R. Weston. Origin of chromitites in the Esker Intrusive Complex, Ring of Fire Intrusive Suite, as revealed by chromite trace element chemistry and simple crystallization models. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328981.
Повний текст джерелаTidd, Alexander N., Richard A. Ayers, Grant P. Course, and Guy R. Pasco. Scottish Inshore Fisheries Integrated Data System (SIFIDS): work package 6 final report development of a pilot relational data resource for the collation and interpretation of inshore fisheries data. Edited by Mark James and Hannah Ladd-Jones. Marine Alliance for Science and Technology for Scotland (MASTS), 2019. http://dx.doi.org/10.15664/10023.23452.
Повний текст джерелаKlement, Eyal, Elizabeth Howerth, William C. Wilson, David Stallknecht, Danny Mead, Hagai Yadin, Itamar Lensky, and Nadav Galon. Exploration of the Epidemiology of a Newly Emerging Cattle-Epizootic Hemorrhagic Disease Virus in Israel. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7697118.bard.
Повний текст джерела