Relevant bibliographies by topics / Tagging algorithm

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Academic literature on the topic 'Tagging algorithm'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Tagging algorithm"

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Mursyit, Mohammad, Aji Prasetya Wibawa, Ilham Ari Elbaith Zaeni, and Harits Ar Rosyid. "Pelabelan Kelas Kata Bahasa Jawa Menggunakan Hidden Markov Model." Mobile and Forensics 2, no. 2 (2020): 71–83. http://dx.doi.org/10.12928/mf.v2i2.2450.

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Part of Speech Tagging atau POS Tagging adalah proses memberikan label pada setiap kata dalam sebuah kalimat secara otomatis. Penelitian ini menggunakan algoritma Hidden Markov Model (HMM) untuk proses POS Tagging. Perlakuan untuk unknown words menggunakan Most Probable POS-Tag. Dataset yang digunakan berupa 10 cerita pendek berbahasa Jawa terdiri dari 10.180 kata yang telah diberikan tagsetBahasa Jawa. Pada penelitian ini proses POS Tagging menggunakan dua skenario. Skenario pertama yaitu menggunakan algoritma Hidden Markov Model (HMM) tanpa menggunakan perlakuan untuk unknow
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Likhomanenko, Tatiana, Denis Derkach, and Alex Rogozhnikov. "Inclusive Flavour Tagging Algorithm." Journal of Physics: Conference Series 762 (October 2016): 012045. http://dx.doi.org/10.1088/1742-6596/762/1/012045.

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Shepitsen, Andriy, and Noriko Tomuro. "Search in Social Tagging Systems Using Ontological User Profiles." Proceedings of the International AAAI Conference on Web and Social Media 3, no. 1 (2009): 315–18. http://dx.doi.org/10.1609/icwsm.v3i1.13978.

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In this paper we present a modified hierarchical agglomerative clustering algorithm for building tag ontologies for social tagging systems. The modified algorithm first uses a clustering algorithm called Domain Similarity Clustering By Committee (DSCBC) (Tomuro et al. 2007) to derive a set of tag committees. We apply DSCBC to the tags entered by the users of social tagging systems and derive (un-ambiguous) committees of tags. Using the committees, a tag ontology is constructed in which an ambiguous tag is separated into multiple, disambiguated tags/nodes. Then a tag profile of a given user is
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Zeng, Xiao Dong, Lidia S. Chao, Derek F. Wong, and Liang Ye He. "iTagger: Part-of-Speech Tagging Based on SBCB Learning Algorithm." Applied Mechanics and Materials 284-287 (January 2013): 3449–53. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3449.

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The problem of part-of-speech (POS) tagging or disambiguation is a practical issue in natural language processing (NLP) community, especially in the development of a machine translation system. The performance of POS tagging system may interference the subsequent analytical tasks in the translation process, and thereafter affects the overall translation quality. This paper presents a novel POS tagging system, iTagger, which is developed based on Selecting Base Classifiers on Bagging (SBCB) learning algorithm. In this work, the POS tagging task is regarded as a classification problem. Features
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Ravi, Sujith, Sergei Vassilivitskii, and Vibhor Rastogi. "Parallel Algorithms for Unsupervised Tagging." Transactions of the Association for Computational Linguistics 2 (December 2014): 105–18. http://dx.doi.org/10.1162/tacl_a_00169.

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We propose a new method for unsupervised tagging that finds minimal models which are then further improved by Expectation Maximization training. In contrast to previous approaches that rely on manually specified and multi-step heuristics for model minimization, our approach is a simple greedy approximation algorithm DMLC (Distributed-Minimum-Label-Cover) that solves this objective in a single step. We extend the method and show how to efficiently parallelize the algorithm on modern parallel computing platforms while preserving approximation guarantees. The new method easily scales to large dat
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Alruqimi, Mohammed, and Noura Aknin. "Enabling social WEB for IoT inducing ontologies from social tagging." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 1 (2019): 19. http://dx.doi.org/10.11591/ijict.v8i1.pp19-24.

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<span>Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to
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Yin, Shao Hong, and Gui Dan Fan. "Research of POS Tagging Rules Mining Algorithm." Applied Mechanics and Materials 347-350 (August 2013): 2836–40. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2836.

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Part of speech contains important grammatical information, so it has great significance for the natural language understanding while the words in the sentence are marked on the parts of speech. POS tagging rules based on statistical methods and rule-based method can mining effectively, but its marked accuracy need to be improved. This paper presents a statistical method and rules of the combination of speech tagging rule mining algorithm in order to improve the correct rate of marked.
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Malyshev, Alexander G., Alexander S. Polygalov, and Sergey A. Alyamkin. "Automatic Tagging of Clothing Images." Vestnik NSU. Series: Information Technologies 18, no. 2 (2020): 54–61. http://dx.doi.org/10.25205/1818-7900-2020-18-2-54-61.

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This paper presents a computer vision clothing auto-tagging algorithm. Tagging is highly demanded in e-commerce as a tool to create a rich uniform set of annotations. The annotations improve catalog organization, statistics, and can be used for interactive catalog search by consumer photos. The proposed algorithm predicts length, design, and color attributes for an arbitrary number of clothing items in an image. The modular structure of the proposed system allows reconfiguration for other sets of tags and tagging tasks not related to clothing.
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Sun, Ming Yang, Wei Feng Sun, Xi Dong Liu, and Lei Xue. "A Novel Personalized Filtering Recommendation Algorithm Based on Collaborative Tagging." Advanced Materials Research 186 (January 2011): 621–25. http://dx.doi.org/10.4028/www.scientific.net/amr.186.621.

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Recommendation algorithms suffer the quality from the huge and sparse dataset. Memory-based collaborative filtering method has addressed the problem of sparsity by predicting unrated values. However, this method increases the computational complexity, sparsity and expensive complexity of computation are trade-off. In this paper, we propose a novel personalized filtering (PF) recommendation algorithm based on collaborative tagging, which weights the feature of tags that show latent personal interests and constructs a top-N tags set to filter out the undersized and dense dataset. The PF recommen
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Zhang, Junyang, Yang Guo, and Xiao Hu. "Research on image tagging algorithm on internet." Cluster Computing 22, S6 (2018): 13619–25. http://dx.doi.org/10.1007/s10586-018-2040-3.

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Dissertations / Theses on the topic "Tagging algorithm"

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Carlson, Paul Chester-John. "Developing a b-jet tagging algorithm for ALICE lessons from CDF /." Click here to view, 2009. http://digitalcommons.calpoly.edu/physsp/4/.

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Thesis (B.S.)--California Polytechnic State University, 2009.<br>Project advisor: Jennifer Klay. Title from PDF title page; viewed on Feb. 4, 2010. Includes bibliographical references. Also available on microfiche.
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Mulligan, Kyle John. "Acceleration of Jaccard’s Index Algorithm for Training to Tag Damage on Post-Earthquake Images." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1934.

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There are currently different efforts to use Supervised Neural Networks (NN) to automatically label damages on images of above ground infrastructure (buildings made of concrete) taken after an earthquake. The goal of the supervised NN is to classify raw input data according to the patterns learned from an input training set. This input training data set is usually supplied by experts in the field, and in the case of this project, structural engineers carefully and mostly manually label these images for different types of damage. The level of expertise of the professionals labeling the training
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Yanik, Banu Deniz. "Next Page Prediction With Popularity Based Page Rank, Duration Based Page Rank And Semantic Tagging Approach." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614097/index.pdf.

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Using page rank and semantic information are frequently used techniques in next page prediction systems. In our work, we extend the use of Page Rank algorithm for next page prediction with several navigational attributes, which are size of the page, duration of the page visit and duration of transition (two page visits sequentially), frequency of page and transition. In our model, we define popularity of transitions and pages by using duration information, use it in a relation with page size, and visit frequency factors. By using the popularity value of pages, we bias conventional Page Rank al
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Mack, Philipp. "Kalibration neuer 'Flavor Tagging' Algorithmen mittels Bs-Oszillationen Calibration of new flavor tagging algorithms using Bs oscillations /." [S.l. : s.n.], 2007. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000007124.

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Kreplin, Katharina [Verfasser], and Stephanie [Akademischer Betreuer] Hansmann-Menzemer. "A Novel Flavour Tagging Algorithm using Machine Learning Techniques and a Precision Measurement of the B0-AntiB0 Oscillation Frequency at the LHCb Experiment / Katharina Kreplin ; Betreuer: Stephanie Hansmann-Menzemer." Heidelberg : Universitätsbibliothek Heidelberg, 2015. http://d-nb.info/1180499492/34.

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Duda, Dominik [Verfasser]. "Identification of bottom-quarks in searches for new heavy resonances decaying into boosted top-quarks with the ATLAS detector and a development of an improved b-tagging algorithm / Dominik Duda." Wuppertal : Universitätsbibliothek Wuppertal, 2016. http://d-nb.info/1104186896/34.

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Krocker, Georg Alexander [Verfasser], та Stephanie [Akademischer Betreuer] Hansmann-Menzemer. "Development and calibration of a same side kaon tagging algorithm and measurement of the B_s^0 – B_s^0 bar oscillation frequency Δm_s at the LHCb experiment / Georg Alexander Krocker ; Betreuer: Stephanie Hansmann-Menzemer". Heidelberg : Universitätsbibliothek Heidelberg, 2013. http://d-nb.info/1177809753/34.

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Zeißner, Sonja Verena [Verfasser], Kevin [Akademischer Betreuer] Kröninger, and Johannes [Gutachter] Albrecht. "Development and calibration of an s-tagging algorithm and its application to constrain the CKM matrix elements |Vts| and |Vtd| in top-quark decays using ATLAS Run-2 Data / Sonja Verena Zeißner ; Gutachter: Johannes Albrecht ; Betreuer: Kevin Kröninger." Dortmund : Universitätsbibliothek Dortmund, 2021. http://d-nb.info/1238349277/34.

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He, Jingwu. "Algorithms for Computational Genetics Epidemiology." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/10.

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The most intriguing problems in genetics epidemiology are to predict genetic disease susceptibility and to associate single nucleotide polymorphisms (SNPs) with diseases. In such these studies, it is necessary to resolve the ambiguities in genetic data. The primary obstacle for ambiguity resolution is that the physical methods for separating two haplotypes from an individual genotype (phasing) are too expensive. Although computational haplotype inference is a well-explored problem, high error rates continue to deteriorate association accuracy. Secondly, it is essential to use a small subset of
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FALABELLA, Antonio. "New Developments of the Flavour Tagging Algorithms for the LHCb Experiment." Doctoral thesis, Università degli studi di Ferrara, 2014. http://hdl.handle.net/11392/2389396.

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Books on the topic "Tagging algorithm"

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Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

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This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described b
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Book chapters on the topic "Tagging algorithm"

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Kwak, Byung-Kwan, and Jeong-Won Cha. "Named Entity Tagging for Korean Using DL-CoTrain Algorithm." In Information Retrieval Technology. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_55.

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Zong, Yu, Guandong Xu, Ping Jin, Peter Dolog, and Shan Jiang. "A Local Information Passing Clustering Algorithm for Tagging Systems." In Database Systems for Adanced Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20244-5_32.

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Lad, Hiteshree, and Mayuri A. Mehta. "Feature Based Object Mining and Tagging Algorithm for Digital Images." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2750-5_36.

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Yuan, Zhenming, Tianhao Yu, and Jia Zhang. "A Social Tagging Based Collaborative Filtering Recommendation Algorithm for Digital Library." In Digital Libraries: For Cultural Heritage, Knowledge Dissemination, and Future Creation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24826-9_25.

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Qin, An, and Wing Shing Wong. "Automatic Segmentation and Tagging of Hanzi Text Using a Hybrid Algorithm." In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. CRC Press, 2022. http://dx.doi.org/10.1201/9780429332111-105.

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Araujo, Lourdes. "Studying the Advantages of a Messy Evolutionary Algorithm for Natural Language Tagging." In Genetic and Evolutionary Computation — GECCO 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45110-2_94.

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Lu, Le, Bing Jian, Dijia Wu, and Matthias Wolf. "A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography." In Machine Learning in Medical Imaging. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02267-3_8.

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Rahman, Nurazzah Abd, Afiqah Bazlla Md Soom, and Normaly Kamal Ismail. "Enhancing Latent Semantic Analysis by Embedding Tagging Algorithm in Retrieving Malay Text Documents." In Advanced Topics in Intelligent Information and Database Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56660-3_27.

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Zhang, Hao, Qiong Hong, Xiaomeng Shi, and Jie He. "A Social Tagging Recommendation Model Based on Improved Artificial Fish Swarm Algorithm and Tensor Decomposition." In Security with Intelligent Computing and Big-data Services. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76451-1_1.

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Sierra Martínez, Luz Marina, Carlos Alberto Cobos, and Juan Carlos Corrales. "Memetic Algorithm Based on Global-Best Harmony Search and Hill Climbing for Part of Speech Tagging." In Mining Intelligence and Knowledge Exploration. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71928-3_20.

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Conference papers on the topic "Tagging algorithm"

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Boltayevich, Elov Botir, Bekmuradova Iroda Zokir qizi, Israilova Saodat Turapovna, and Toirova Guli Ibragimovna. "Tagging Units in the Text and the Bayes Algorithm." In 2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE). IEEE, 2024. https://doi.org/10.1109/piere62470.2024.10804863.

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Zhao, Wan. "Algorithm of Part-of-Speech Tagging of Corpus Based on Recurrent Neural Network." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721793.

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Li, Hui, and Zhijing Wu. "Algorithm of Part-of-Speech Tagging of Corpus based on HMM Model." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10939572.

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Park, Youchan, Kyeongsun Kim, Hyunseung Rhee, Wonjik Shin, and Hyungrok Do. "One-dimensional Seedless Velocimetry in a N2-enriched Subsonic Flow using Femtosecond Electronics Excitation Tagging." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.af3i.6.

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Spatially elongated femtosecond laser-induced plasma electronically excites nitrogen molecules generating visible range fluorescence. The fluorescence temporally traces the displacement of the nitrogen molecules enabling one-dimensional velocimetry aided by decomposition-based denoising and center-detecting algorithm.
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Diekmann, Svenja. "Calibration of Flavour Tagging Algorithms at the CMS Experiment - Run 3 Results and Innovations." In 12th Large Hadron Collider Physics Conference. Sissa Medialab, 2025. https://doi.org/10.22323/1.478.0263.

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Huynh, Quang Duc, Phuoc Tran, and Huu Nguyen. "A SENSE TAGGING ALGORITHM USING UNSUPERVISED METHOD." In NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN. Publishing House for Science and Technology, 2019. http://dx.doi.org/10.15625/vap.2019.0001.

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VACAVANT, Laurent. "b-tagging algorithm and performance in ATLAS." In Physics at LHC 2008. Sissa Medialab, 2010. http://dx.doi.org/10.22323/1.055.0064.

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Yin, Shaohong, and Guidan Fan. "Research of POS Tagging Rules Mining Algorithm." In 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013). Atlantis Press, 2013. http://dx.doi.org/10.2991/iccsee.2013.488.

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Forsati, Rana, Mehrnoush Shamsfard, and Pouyan Mojtahedpour. "An Efficient Meta Heuristic Algorithm for POS-tagging." In 2010 5th International Multi-Conference on Computing in the Global Information Technology (ICCGI). IEEE, 2010. http://dx.doi.org/10.1109/iccgi.2010.42.

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Tanasini, Martino. "New ATLAS b-tagging algorithm for Run 3." In 41st International Conference on High Energy physics. Sissa Medialab, 2022. http://dx.doi.org/10.22323/1.414.1090.

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Reports on the topic "Tagging algorithm"

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Gronberg, J., and J. Hollar. Trigger Algorithm Design for a SUSY Lepton Trigger based on Forward Proton Tagging. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/975215.

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Das, Mayukh. Developing a b-tagging algorithm using soft muons at level-3 for the DO detector at Fermilab. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/1415854.

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Salamanna, Giuseppe. Study of Bs mixing at the CDFII experiment with a newly developed opposite side b-flavour tagging algorithm using kaons. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/897034.

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Shah, Tushar. Measurement of $B^0$ Mixing Frequency Using a New Probability Based Self-Tagging Algorithm Applied to Inclusive Lepton Events from $p\bar{p}$ Collisions at $\sqrt{s}$ = 1.8-TeV. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/1371860.

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Mack, Philipp. Calibration of new flavor tagging algorithms using Bs oscillations. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/920111.

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Diesner, Jana, and Kathleen M. Carley. Looking Under the Hood of Stochastic Machine Learning Algorithms for Parts of Speech Tagging. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada487511.

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