Relevant bibliographies by topics / Classification methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Classification methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Classification methods"

1

Kubade, Harshad M. "The Overview of Bayes Classification Methods." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2801–2. http://dx.doi.org/10.31142/ijtsrd15750.

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Crouch, R. J., and R. J. Blong. "Gully sidewall classification: methods and applications." Zeitschrift für Geomorphologie 33, no. 3 (October 5, 1989): 291–305. http://dx.doi.org/10.1127/zfg/33/1989/291.

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Ghimire, Santosh. "On the Image Pixels Classification Methods." Journal of the Institute of Engineering 15, no. 2 (July 31, 2019): 202–9. http://dx.doi.org/10.3126/jie.v15i2.27667.

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In this article, we first discuss about the images and image pixels classifications. Then we briefly discuss the importance of classification of images and finally focus on various methods of classification which can be implemented to classify image pixels.
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Rasheed, Nishana, and Shreeja R. "Image Classification Methods." International Journal of Engineering Trends and Technology 8, no. 8 (February 25, 2014): 461–64. http://dx.doi.org/10.14445/22315381/ijett-v8p279.

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Pekhtasheva, Elena, Anatoly Neverov, Stefan Kubica, and Gennady Zaikov. "Classification of Biodamages, Evaluation and Protection Methods." Chemistry & Chemical Technology 6, no. 4 (December 20, 2012): 459–72. http://dx.doi.org/10.23939/chcht06.04.459.

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Othman, Osama M., and Christopher H. Bryant. "Pruning Classification Rules with Instance Reduction Methods." International Journal of Machine Learning and Computing 5, no. 3 (June 2015): 187–91. http://dx.doi.org/10.7763/ijmlc.2015.v5.505.

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Babaeva, Vasila. "Error Classification And Methods Of Their Correction." American Journal of Social Science and Education Innovations 02, no. 08 (August 29, 2020): 474–77. http://dx.doi.org/10.37547/tajssei/volume02issue08-76.

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Perepelitsa, V. A., I. V. Kozin, and S. V. Kurapov. "Methods of classification and algorithms of graph coloring." Researches in Mathematics 16 (February 7, 2021): 135. http://dx.doi.org/10.15421/240816.

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We study the connection between classifications on finite set and the problem of graph coloring. We consider the optimality criterion for classification of special type: h-classifications, which are built on the base of proximity measure. It is shown that the problem of finding the optimal h-classification can be reduced to the problem of coloring of non-adjacency graph vertices by the smallest possible number of colors. We consider algorithms of proper coloring of graph vertices.
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Marini, Federico. "Classification Methods in Chemometrics." Current Analytical Chemistry 6, no. 1 (January 1, 2010): 72–79. http://dx.doi.org/10.2174/157341110790069592.

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WATADA, Junzo. "Methods for Fuzzy Classification." Journal of Japan Society for Fuzzy Theory and Systems 4, no. 1 (1992): 61–73. http://dx.doi.org/10.3156/jfuzzy.4.1_61.

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Dissertations / Theses on the topic "Classification methods"

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Jamain, Adrien. "Meta-analysis of classification methods." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413686.

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Chzhen, Evgenii. "Plug-in methods in classification." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC2027/document.

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Ce manuscrit étudie plusieurs problèmes de classification sous contraintes. Dans ce cadre de classification, notre objectif est de construire un algorithme qui a des performances aussi bonnes que la meilleure règle de classification ayant une propriété souhaitée. Fait intéressant, les méthodes de classification de type plug-in sont bien appropriées à cet effet. De plus, il est montré que, dans plusieurs configurations, ces règles de classification peuvent exploiter des données non étiquetées, c'est-à-dire qu'elles sont construites de manière semi-supervisée. Le Chapitre 1 décrit deux cas particuliers de la classification binaire - la classification où la mesure de performance est reliée au F-score, et la classification équitable. A ces deux problèmes, des procédures semi-supervisées sont proposées. En particulier, dans le cas du F-score, il s'avère que cette méthode est optimale au sens minimax sur une classe usuelle de distributions non-paramétriques. Aussi, dans le cas de la classification équitable, la méthode proposée est consistante en terme de risque de classification, tout en satisfaisant asymptotiquement la contrainte d’égalité des chances. De plus, la procédure proposée dans ce cadre d'étude surpasse en pratique les algorithmes de pointe. Le Chapitre 3 décrit le cadre de la classification multi-classes par le biais d'ensembles de confiance. Là encore, une procédure semi-supervisée est proposée et son optimalité presque minimax est établie. Il est en outre établi qu'aucun algorithme supervisé ne peut atteindre une vitesse de convergence dite rapide. Le Chapitre 4 décrit un cas de classification multi-labels dans lequel on cherche à minimiser le taux de faux-négatifs sous réserve de contraintes de type presque sûres sur les règles de classification. Dans cette partie, deux contraintes spécifiques sont prises en compte: les classifieurs parcimonieux et ceux soumis à un contrôle des erreurs négatives à tort. Pour les premiers, un algorithme supervisé est fourni et il est montré que cet algorithme peut atteindre une vitesse de convergence rapide. Enfin, pour la seconde famille, il est montré que des hypothèses supplémentaires sont nécessaires pour obtenir des garanties théoriques sur le risque de classification
This manuscript studies several problems of constrained classification. In this frameworks of classification our goal is to construct an algorithm which performs as good as the best classifier that obeys some desired property. Plug-in type classifiers are well suited to achieve this goal. Interestingly, it is shown that in several setups these classifiers can leverage unlabeled data, that is, they are constructed in a semi-supervised manner.Chapter 2 describes two particular settings of binary classification -- classification with F-score and classification of equal opportunity. For both problems semi-supervised procedures are proposed and their theoretical properties are established. In the case of the F-score, the proposed procedure is shown to be optimal in minimax sense over a standard non-parametric class of distributions. In the case of the classification of equal opportunity the proposed algorithm is shown to be consistent in terms of the misclassification risk and its asymptotic fairness is established. Moreover, for this problem, the proposed procedure outperforms state-of-the-art algorithms in the field.Chapter 3 describes the setup of confidence set multi-class classification. Again, a semi-supervised procedure is proposed and its nearly minimax optimality is established. It is additionally shown that no supervised algorithm can achieve a so-called fast rate of convergence. In contrast, the proposed semi-supervised procedure can achieve fast rates provided that the size of the unlabeled data is sufficiently large.Chapter 4 describes a setup of multi-label classification where one aims at minimizing false negative error subject to almost sure type constraints. In this part two specific constraints are considered -- sparse predictions and predictions with the control over false negative errors. For the former, a supervised algorithm is provided and it is shown that this algorithm can achieve fast rates of convergence. For the later, it is shown that extra assumptions are necessary in order to obtain theoretical guarantees in this case
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Gimati, Yousef M. T. "Bootstrapping techniques to improve classification methods." Thesis, University of Leeds, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401072.

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Kobayashi, Izumi. "Randomized ensemble methods for classification trees." Diss., Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FKobayashi.pdf.

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Thesis (Ph. D. in Operations Research)--Naval Postgraduate School, September 2002.
Dissertation supervisor: Samuel E. Buttrey. Includes bibliographical references (p. 117-119). Also available online.
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Baker, Jonathan Peter. "Methods of Music Classification and Transcription." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3330.

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We begin with an overview of some signal processing terms and topics relevant to music analysis including facts about human sound perception. We then discuss common objectives of music analysis and existing methods for accomplishing them. We conclude with an introduction to a new method of automatically transcribing a piece of music from a digital audio signal.
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Clibbon, Alex P. "Methods of classification of the cardiotocogram." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:550bb5ea-bee8-4eb8-95e2-f16c54d7cd68.

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This Thesis compares CTG classification techniques proposed in the literature and their potential extensions. A comparison between four classifiers previously assessed - Adaboost, Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM) - and two proposed classifiers - Bayesian ANN (BANN), Relevance Vector Machine - was conducted using a database of 7,568 cases and two open source databases. The Random Forest (RF) achieved the highest average result and was proposed as a benchmark classifier. The proposal to use model certainty to introduce a third, unclassified, class was investigated using the BANN. An increase in the classification accuracy was demonstrated, however the proportion of cases in the unclassified class was too great to be of practical value. The information content of time series was explored using a Hidden Markov Model (HMM). The average performance of the HMM was comparable with the performance of the benchmark with a smaller distribution across validation folds, demonstrating that time-series information provides more stable estimates of class than stationary methods. Finally a method of system identification was implemented. Significant differences between feature trends and histograms in low pH (< 7.1) and healthy pH (≥ 7.1) cases were observed. These features were used as classifier inputs, and achieved performance similar to existing feature sets. When these features were aligned according the onset of stage 2 labour three unique trend patterns were discovered.
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Felldin, Markus. "Machine Learning Methods for Fault Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183132.

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This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future.
Detta examensarbete, utfört på Ericsson AB, ämnar att undersöka huruvida maskininlärningstekniker kan användas för att klassificera dumpfiler för mer effektiv problemidentifiering. Projektet fokuserar på övervakad inlärning och då speciellt Bayesiansk klassificering. Arbetet visar att ett program som utnyttjar Bayesiansk klassificering kan uppnå en noggrannhet väl över slumpen. Arbetet indikerar att maskininlärningstekniker mycket väl kan komma att bli användbara alternativ till mänsklig klassificering av dumpfiler i en nära framtid.
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Beghtol, Clare. "James Duff Brown's Subject Classification and Evaluation Methods for Classification Systems." dLIST, 2004. http://hdl.handle.net/10150/106250.

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James Duff Brown (1862-1914), an important figure in librarianship in late nineteenth and early twentieth century England, made contributions in many areas of his chosen field. His Subject Classification (SC), however, has not received much recognition for its theoretical and practical contributions to bibliographic classification theory and practice in the twentieth century. This paper discusses some of the elements of SC that both did and did not inform future bibliographic classification work, considers some contrasting evaluation methods in the light of advances in bibliographic classification theory and practice and of commentaries on SC, and suggests directions for further research.
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Ravindran, Sourabh. "Physiologically Motivated Methods For Audio Pattern Classification." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14066.

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Human-like performance by machines in tasks of speech and audio processing has remained an elusive goal. In an attempt to bridge the gap in performance between humans and machines there has been an increased effort to study and model physiological processes. However, the widespread use of biologically inspired features proposed in the past has been hampered mainly by either the lack of robustness across a range of signal-to-noise ratios or the formidable computational costs. In physiological systems, sensor processing occurs in several stages. It is likely the case that signal features and biological processing techniques evolved together and are complementary or well matched. It is precisely for this reason that modeling the feature extraction processes should go hand in hand with modeling of the processes that use these features. This research presents a front-end feature extraction method for audio signals inspired by the human peripheral auditory system. New developments in the field of machine learning are leveraged to build classifiers to maximize the performance gains afforded by these features. The structure of the classification system is similar to what might be expected in physiological processing. Further, the feature extraction and classification algorithms can be efficiently implemented using the low-power cooperative analog-digital signal processing platform. The usefulness of the features is demonstrated for tasks of audio classification, speech versus non-speech discrimination, and speech recognition. The low-power nature of the classification system makes it ideal for use in applications such as hearing aids, hand-held devices, and surveillance through acoustic scene monitoring
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Kim, Heeyoung. "Statistical methods for function estimation and classification." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44806.

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This thesis consists of three chapters. The first chapter focuses on adaptive smoothing splines for fitting functions with varying roughness. In the first part of the first chapter, we study an asymptotically optimal procedure to choose the value of a discretized version of the variable smoothing parameter in adaptive smoothing splines. With the choice given by the multivariate version of the generalized cross validation, the resulting adaptive smoothing spline estimator is shown to be consistent and asymptotically optimal under some general conditions. In the second part, we derive the asymptotically optimal local penalty function, which is subsequently used for the derivation of the locally optimal smoothing spline estimator. In the second chapter, we propose a Lipschitz regularity based statistical model, and apply it to coordinate measuring machine (CMM) data to estimate the form error of a manufactured product and to determine the optimal sampling positions of CMM measurements. Our proposed wavelet-based model takes advantage of the fact that the Lipschitz regularity holds for the CMM data. The third chapter focuses on the classification of functional data which are known to be well separable within a particular interval. We propose an interval based classifier. We first estimate a baseline of each class via convex optimization, and then identify an optimal interval that maximizes the difference among the baselines. Our interval based classifier is constructed based on the identified optimal interval. The derived classifier can be implemented via a low-order-of-complexity algorithm.
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Books on the topic "Classification methods"

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Pattern classification using ensemble methods. Singapore: World Scientific, 2010.

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Doumpos, Michael. Multicriteria decision aid classification methods. Dordrecht: Kluwer Academic Publishers, 2002.

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Holeňa, Martin, Petr Pulc, and Martin Kopp. Classification Methods for Internet Applications. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36962-0.

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Constantin, Zopounidis, ed. Multicriteria decision aid classification methods. Dordrecht: Kluwer Academic Publishers, 2002.

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M, Mather Paul, ed. Classification methods for remotely sensed data. 2nd ed. Boca Raton, FL: CRC Press, 2009.

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Alfaro, Esteban, Matías Gámez, and Noelia García, eds. Ensemble Classification Methods with Applicationsin R. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119421566.

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Kiers, Henk A. L., Jean-Paul Rasson, Patrick J. F. Groenen, and Martin Schader, eds. Data Analysis, Classification, and Related Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59789-3.

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Hayashi, Chikio, Keiji Yajima, Hans-Hermann Bock, Noboru Ohsumi, Yutaka Tanaka, and Yasumasa Baba, eds. Data Science, Classification, and Related Methods. Tokyo: Springer Japan, 1998. http://dx.doi.org/10.1007/978-4-431-65950-1.

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1929-, Agrawal S. P., ed. Book numbers: Some Indian methods. New Delhi: Concept Pub. Co., 1990.

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Particle size analysis: Classification and sedimentation methods. London: Chapman & Hall, 1994.

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Book chapters on the topic "Classification methods"

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Das, Sibanjan. "Classification Methods." In Data Science Using Oracle Data Miner and Oracle R Enterprise, 189–237. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2614-8_6.

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Dougherty, Geoff. "Nonmetric Methods." In Pattern Recognition and Classification, 27–41. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5323-9_3.

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Good, Phillip I. "Classification and Discrimination." In Resampling Methods, 164–86. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4757-3049-4_10.

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Canela, Miguel Ángel, Inés Alegre, and Alberto Ibarra. "Classification Models." In Quantitative Methods for Management, 75–82. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17554-2_8.

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Rizzi, Alfredo. "Statistical Methods for Cryptography." In Data Analysis and Classification, 13–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_2.

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Abe, Shigeo. "Training Methods." In Support Vector Machines for Pattern Classification, 227–303. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-098-4_5.

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Masters, Timothy. "Gating Methods." In Assessing and Improving Prediction and Classification, 393–416. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3336-8_8.

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Abe, Shigeo. "Kernel-Based Methods Kernel@Kernel-based method." In Support Vector Machines for Pattern Classification, 305–29. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-098-4_6.

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Kiasaleh, Kamran. "Signal Decomposition Methods." In Biological Signals Classification and Analysis, 277–376. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_5.

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Ruczinski, Ingo, Charles Kooperberg, and Michael LeBlanc. "Logic Regression — Methods and Software." In Nonlinear Estimation and Classification, 333–43. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21579-2_21.

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Conference papers on the topic "Classification methods"

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Horte, T., R. Skjong, P. Friis-Hansen, A. P. Teixeira, and F. Viejo de Francisco. "Probabilistic Methods Applied To Structural Design And Rule Development." In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.07.

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Such, Ondrej, Santiago Barreda, Martin Klimo, Peter Tarabek, and Andrea Tinajova. "Comparing Classification Methods in Isolated Vowel Classification." In 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA). IEEE, 2018. http://dx.doi.org/10.1109/disa.2018.8490601.

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Gilmer, John, and Jianhua Chen. "Unsupervised semantic classification methods." In 2011 IEEE International Conference on Granular Computing (GrC-2011). IEEE, 2011. http://dx.doi.org/10.1109/grc.2011.6122595.

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Schlichenmaier, Martin, Piotr Kielanowski, Anatol Odzijewicz, Martin Schlichenmaier, and Theodore Voronov. "Classification of central extensions of Lax operator algebras." In GEOMETRIC METHODS IN PHYSICS. AIP, 2008. http://dx.doi.org/10.1063/1.3043863.

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Dessauer, Michael P., and Sumeet Dua. "Discriminative features and classification methods for accurate classification." In SPIE Defense, Security, and Sensing, edited by Teresa H. O'Donnell, Misty Blowers, and Kevin L. Priddy. SPIE, 2010. http://dx.doi.org/10.1117/12.853267.

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Pan, Yuzhu. "Research on Leaf Classification under Different Classification Methods." In 2021 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2021. http://dx.doi.org/10.1109/icpics52425.2021.9524160.

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Vondra, Jan, Piotr Kielanowski, Anatol Odzijewicz, Martin Schlichenmaier, and Theodore Voronov. "Classification of principal connections on W[sup r]PE." In GEOMETRIC METHODS IN PHYSICS. AIP, 2008. http://dx.doi.org/10.1063/1.3043858.

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"RECONSTRUCTING IVUS IMAGES FOR AN ACCURATE TISSUE CLASSIFICATION." In Computer Vision Methods in Medicine. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002061001130119.

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Ruta, Dymitr, Bogdan Gabrys, George Maroulis, and Theodore E. Simos. "Reducing Spatial Data Complexity for Classification Models." In Computational Methods in Science and Engineering. AIP, 2007. http://dx.doi.org/10.1063/1.2827047.

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Nakano, Felipe kenji, Saulo Martiello Mastelini, Sylvio Barbon, and Ricardo Cerri. "Stacking Methods for Hierarchical Classification." In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. http://dx.doi.org/10.1109/icmla.2017.0-145.

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Reports on the topic "Classification methods"

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Latifovic, R., J. Cihlar, and J. Beaubien. Clustering Methods for Unsupervised Classification. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219517.

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Eom, K., M. Wellman, N. Srour, D. Hillis, and R. Chellappa. Acoustic Target Classification Using Multiscale Methods. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada358579.

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Stamp, Jason Edwin, and Philip LaRoche Campbell. A classification scheme for risk assessment methods. Office of Scientific and Technical Information (OSTI), August 2004. http://dx.doi.org/10.2172/925643.

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Firth, Robert, Bill Wood, Rich Pethia, Lauren Roberts, and Vicky Mosley. A Classification Scheme for Software Development Methods. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada200606.

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Ryan, F. M., D. N. Anderson, K. K. Anderson, D. N. Hagedorn, K. T. Higbee, N. E. Miller, T. Redgate, and A. C. Rohay. Statistical classification methods applied to seismic discrimination. Office of Scientific and Technical Information (OSTI), June 1996. http://dx.doi.org/10.2172/257361.

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Irizarry, Alfredo V. Optimal Methods for Classification of Digitally Modulated Signals. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada583399.

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Iagnemma, Karl. Investigation of Terrain Analysis and Classification Methods for Ground Vehicles. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada577237.

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Grother, P. J. Comparison of FFT fingerprint filtering methods for neural network classification. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5493.

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Hedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.

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Irizarry, Alfredo V. Average Likelihood Methods of Classification of Code Division Multiple Access (CDMA). Fort Belvoir, VA: Defense Technical Information Center, May 2016. http://dx.doi.org/10.21236/ad1009582.

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