Relevant bibliographies by topics / Classification methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Classification methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Classification methods"

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Ghimire, Santosh. "On the Image Pixels Classification Methods." Journal of the Institute of Engineering 15, no. 2 (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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Crouch, R. J., and R. J. Blong. "Gully sidewall classification: methods and applications." Zeitschrift für Geomorphologie 33, no. 3 (1989): 291–305. http://dx.doi.org/10.1127/zfg/33/1989/291.

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

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Hrytsiuk, Petro, Tetiana Babych, and Bohdan Krasko. "CLASSIFICATION METHODS OF THE YIELD FORECASTING." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (2022): 209–16. http://dx.doi.org/10.31891/2307-5732-2022-309-3-209-210.

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Analysis of wheat yields over the past 20 years shows that wheat yield are growing in all regions of Ukraine. This is a result of the growing investment attractiveness of the grain industry and the significant investments made in the industry. However, the upward trend is accompanied by significant fluctuations in wheat yields, which are mostly caused by weather and climatic factors. The most noticeable is the climatic factors influence for the steppe regions of Ukraine. The paper proposes a classification approach to forecasting wheat yield taking into account weather and climatic factors and
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Rasheed, Nishana, and Shreeja R. "Image Classification Methods." International Journal of Engineering Trends and Technology 8, no. 8 (2014): 461–64. http://dx.doi.org/10.14445/22315381/ijett-v8p279.

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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 (2015): 187–91. http://dx.doi.org/10.7763/ijmlc.2015.v5.505.

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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 (2012): 459–72. http://dx.doi.org/10.23939/chcht06.04.459.

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Babaeva, Vasila. "Error Classification And Methods Of Their Correction." American Journal of Social Science and Education Innovations 02, no. 08 (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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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 parti
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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.<br>Detta examensarbete, utfört på Ericsson AB, ämnar
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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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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, clas
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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.<br>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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Zeggada, Abdallah. "Advanced classification methods for UAV imagery." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/367947.

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The rapid technological advancement manifested lately in the remote sensing acquisition platforms has triggered many benefits in favor of automated territory control and monitoring. In particular, unmanned aerial vehicles (UAVs) technology has drawn a lot of attention, providing an efficient solution especially in real-time applications. This is mainly motivated by their capacity to collect extremely high resolution (EHR) data over inaccessible areas and limited coverage zones, thanks to their small size and rapidly deployable flight capability, notwithstanding their ease of use and affordabil
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Zeggada, Abdallah. "Advanced classification methods for UAV imagery." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2943/1/thesis_disclaimer.pdf.

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The rapid technological advancement manifested lately in the remote sensing acquisition platforms has triggered many benefits in favor of automated territory control and monitoring. In particular, unmanned aerial vehicles (UAVs) technology has drawn a lot of attention, providing an efficient solution especially in real-time applications. This is mainly motivated by their capacity to collect extremely high resolution (EHR) data over inaccessible areas and limited coverage zones, thanks to their small size and rapidly deployable flight capability, notwithstanding their ease of use and affordabil
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Lugo, A. "ORDINAL CLASSIFICATION TREES: METHODS AND APPLICATION." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/344318.

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During my PhD in Biomedical Statistics, I focused my research on the study of ordinal classification tree methodology, to be applied in case of ordinal categorical outcome, and all the statistical and computational methods related to them. Then, I provided an application on the set of controls of Italian case-control studies, in order to identify the profiles of individuals according to their total energy intake, and to their consumption of red meat and processed meat. Introduction to classification trees Tree-based methods are non parametric regression methods belonging to a group of tech
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Books on the topic "Classification methods"

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

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

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

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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. Springer Japan, 1998. http://dx.doi.org/10.1007/978-4-431-65950-1.

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Alfaro, Esteban, Matías Gámez, and Noelia García, eds. Ensemble Classification Methods with Applicationsin R. 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. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59789-3.

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L, Kiers Henk A., and International Federation of Classification Societies. Conference, eds. Data analysis, classification, and related methods. Springer, 2000.

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

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Bernhardt, Claus. Particle Size Analysis: Classification and sedimentation methods. Springer Netherlands, 1994.

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T, Denison David G., ed. Bayesian methods for nonlinear classification and regression. Wiley, 2002.

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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. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2614-8_6.

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Rengaswamy, Raghunathan, and Resmi Suresh. "Classification Methods." In Data Science for Engineers. CRC Press, 2022. http://dx.doi.org/10.1201/b23276-7.

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Dougherty, Geoff. "Nonmetric Methods." In Pattern Recognition and Classification. 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. Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4757-3049-4_10.

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

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

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

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

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

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

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Conference papers on the topic "Classification methods"

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Ganiyu, Aishat, Iman Darvishi, Ronald Addo-Quaye, Abel Yeboah-Ofori, Bismark Tei Asare, and Oluwole Oguntoyinbo. "Classification Algorithms Using Ensemble Methods." In 2024 11th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2024. http://dx.doi.org/10.1109/ficloud62933.2024.00033.

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Sharabas, Youssef Hazem Mohamed Fathi, Yasmine Abou Ouf Hamed, and Zeinab Abd El Haliem Taha. "Multiple Sclerosis (MS) Classification and Detection." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652740.

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Sennad, Meriem, Zineb Ellaky, and Faouzia Benabbou. "Enhancing DarkWebActivities Classification Using Embedding Methods." In 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2025. https://doi.org/10.1109/iraset64571.2025.11008275.

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Ponomareva, Olga, Yurii Birukov, Mikchail Gribov, Maksim Chistyakov, and Dmitrii Barybin. "Overview of network traffic classification methods." In 2025 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). IEEE, 2025. https://doi.org/10.1109/usbereit65494.2025.11054089.

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Folorunso, Serifat, Hiba Alsmadi, Ala S. Al Kafari, and Gok Kandasamy. "Autistic Spectrum Disorder Screening Classification with Machine Learning Approaches." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652779.

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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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BANG HUSEBY, R., O. MILVANG, A. SOLBERG, and K. WEISTEEN. "SEABED CLASSIFICATION FROM BACKSCATTER SONAR DATA USING STATISTICAL METHODS." In Acoustic Classification and Mapping of the Seabed 1993. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/20446.

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Emam, Ahmed Mohamed, Omar Tarek Ali, and Ayman Atia. "Football activities classification." In 2023 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2023. http://dx.doi.org/10.1109/imsa58542.2023.10217464.

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KAVLI, T., M. CARLIN, and R. MADSEN. "SEABED CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS AND OTHER NONPARAMETRIC METHODS." In Acoustic Classification and Mapping of the Seabed 1993. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/20456.

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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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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. Defense Technical Information Center, 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), 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. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada200606.

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Ryan, F. M., D. N. Anderson, K. K. Anderson, et al. Statistical classification methods applied to seismic discrimination. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/257361.

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

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Iagnemma, Karl. Investigation of Terrain Analysis and Classification Methods for Ground Vehicles. Defense Technical Information Center, 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. 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, 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). Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1009582.

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