Academic literature on the topic 'Tree of decisions'
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Journal articles on the topic "Tree of decisions"
Scott, Jessie, and David Betters. "Economic Analysis of Urban Tree Replacement Decisions." Arboriculture & Urban Forestry 26, no. 2 (March 1, 2000): 69–77. http://dx.doi.org/10.48044/jauf.2000.008.
Full textTOFAN, Cezarina Adina. "Method of decision tree applied in adopting the decision for promoting a company." Annals of "Spiru Haret". Economic Series 15, no. 3 (September 30, 2015): 47. http://dx.doi.org/10.26458/1535.
Full textAMPUŁA, Dariusz. "Prediction of Post-Diagnostic Decisions for Tested Hand Grenades’ Fuzes Using Decision Trees." Problems of Mechatronics Armament Aviation Safety Engineering 12, no. 2 (June 30, 2021): 39–54. http://dx.doi.org/10.5604/01.3001.0014.9332.
Full textJiang, Daniel R., Lina Al-Kanj, and Warren B. Powell. "Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds." Operations Research 68, no. 6 (November 2020): 1678–97. http://dx.doi.org/10.1287/opre.2019.1939.
Full textLi, Jiawei, Yiming Li, Xingchun Xiang, Shu-Tao Xia, Siyi Dong, and Yun Cai. "TNT: An Interpretable Tree-Network-Tree Learning Framework using Knowledge Distillation." Entropy 22, no. 11 (October 24, 2020): 1203. http://dx.doi.org/10.3390/e22111203.
Full textHeshmatol Vaezin, S. M., J. L. Peyron, and F. Lecocq. "A simple generalization of the Faustmann formula to tree level." Canadian Journal of Forest Research 39, no. 4 (April 2009): 699–711. http://dx.doi.org/10.1139/x08-202.
Full textRautenberg, Tamlyn, Annette Gerritsen, and Martin Downes. "Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer." Diagnostics 10, no. 3 (March 14, 2020): 158. http://dx.doi.org/10.3390/diagnostics10030158.
Full textMarzouk, Mohamed, and Emad Mohamed. "Modeling bid/no bid decisions using fuzzy fault tree." Construction Innovation 18, no. 1 (January 2, 2018): 90–108. http://dx.doi.org/10.1108/ci-11-2016-0060.
Full textAbate, Tensaye, and Temesgen Yohannes. "Socio-Economic Determinants of Smallholder Tree Plantation in Basona-Werana Woreda in the North Shoa of Amhara Regional State, Ethiopia." Caraka Tani: Journal of Sustainable Agriculture 37, no. 1 (November 22, 2021): 15. http://dx.doi.org/10.20961/carakatani.v37i1.54247.
Full textLuna, José Marcio, Efstathios D. Gennatas, Lyle H. Ungar, Eric Eaton, Eric S. Diffenderfer, Shane T. Jensen, Charles B. Simone, Jerome H. Friedman, Timothy D. Solberg, and Gilmer Valdes. "Building more accurate decision trees with the additive tree." Proceedings of the National Academy of Sciences 116, no. 40 (September 16, 2019): 19887–93. http://dx.doi.org/10.1073/pnas.1816748116.
Full textDissertations / Theses on the topic "Tree of decisions"
Nilsson, Lannerstedt Katarina. "Location decisions regarding forest plantations in Brazil : Which aspects are important to actors in the Brazilian tree industry?" Thesis, KTH, Hållbar utveckling, miljövetenskap och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266973.
Full textDenna studie handlar om brasilianska skogsplanteringar och expansionen av planteringar som ägs av företag i landets, så kallade ”trädindustri”. Studien fokuserar på produktiva skogsplanteringar av introducerade arter, för vilka intresset har vuxit parallellt med att de har expanderat till yta under de senaste decennierna. Intresset har även vuxit eftersom federala beslutsfattare har lyft fram skogsplanteringar som ett verktyg för att minska landets koldioxidutsläpp och stimulera ekonomisk utveckling. Produktiva brasilianska skogsplanteringar och hållbarhet är ett kontinuerligt föremål för debatt. Den befintliga litteraturen om expansionen av sådana planteringar indikerar att företag i landets trädindustri inte alltid har balanserat de ekonomiska, miljömässiga och sociala hållbarhetsdimensionerna i sina beslut gällande var planteringar ska anläggas. I vissa fall har besluten resulterat i negativa följder för lokala ekosystem och samhällen. Vidare finns det, såvitt författaren vet, inga studier som behandlar lokaliseringsbeslut gällande sådana brasilianska skogsplanteringar och alla de tre hållbarhetsdimensionerna. Därför syftar denna studie till att förbättra förståelsen för det samtida beslutssammanhang i vilket företag i den brasilianska trädindustrin väljer platser för sina skogsplanteringar, samt hur hållbarhetsaspekter omfattas i sådana beslut. Som ett led i denna strävan undersöks möjliga platsfaktorer, rumsliga begränsningar och andra beslutsaspekter, liksom vilken roll företag tilldelar specifika mekanismer för hållbar utveckling, såsom brasiliansk policy, lagstiftning och oberoende skogscertifiering. En kvalitativ forskningsstrategi antas för att genomföra undersökningen. En litteraturöversikt kombineras med semistrukturerade intervjuer med branschutövare i två segment av den brasilianska trädindustrin. Deras uppfattningar trianguleras med perspektiv från relevanta aktörer utanför branschen. Urvalet av deltagarna för studien genomfördes på plats i Brasilien 2015 till 2016 och resulterade i 13 brasilianska intervjudeltagare. Intervjuerna genomfördes sedan på olika platser i Brasilien under 2016. Grundad teori används som forskningsmetod för att analysera insamlad kvalitativ data. Två huvudteman, flera sekundära teman och ett koncept härleds från de kvalitativa data som samlades in, vilka främst grundar sig i uppfattningar gällande brasilianska massa- och pappersföretag. Flera beslutsfaktorer och begränsningar som kan påverka placeringen av brasilianska skogsplanteringar identifieras också. Det kan konstateras att aktörerna i urvalet i studien uppfattar det som att strategiska, ekonomiska, miljömässiga och sociala perspektiv är närvarande i dagens lokaliseringsbeslut, utförda av certifierade, brasilianska massa- och pappersföretag. En viktig begränsning i studien är att dess utforskande karaktär hindrar författaren från att presentera några ”sanningar” om ämnet som undersöks, eller att dra slutsatser om företagens verkliga handlingar. Därför presenteras istället ett antal hypoteser som gäller Brasilien, men även en hypotes av generell karaktär. Den generella hypotesen är att skogsplanteringar kan vara föremål för integrerad lokaliserings- och hållbarhetsanalys, där problemet kan formuleras som att hitta optimala platser för skogsplanteringar ur ett tredimensionellt hållbarhetsperspektiv. Med tanke på begränsningarna i studien, samt associerade osäkerheter som hypoteserna gällande det brasilianska fallet är befästa med, är rekommendationen att fortsatta studier först koncentrerar sig på att testa den allmänna hypotesen. Om fortsatta studier på nationell nivå visar sig vara fördelaktiga efter sådana tester, uppmuntras forskare att återvända till sammanfattningen av branschperspektiv, återstående frågor och hypoteser som tillgängliggörs för fortsatt forskning om Brasilien genom denna studie.
Este estudo trata das florestas plantadas brasileiras, e da expansão de plantios pertencentes a empresas da indústria brasileira de árvores plantadas. O estudo tem foco nas florestas plantadas produtivas de espécies introduzidas, pelas quais se teve um aumento no interesse paralelamente à sua expansão geográfica nas últimas décadas. O interesse também aumentou ao destaque dado aos plantios florestais pelo governo federal como uma ferramenta para reduzir as emissões de dióxido de carbono do país, e estimular o desenvolvimento econômico. As florestas plantadas produtivas brasileiras e a sustentabilidade são constantemente temas de debate. A literatura existente sobre essa expansão indica que as empresas da indústria brasileira de árvores plantadas nem sempre equilibram as três dimensões de sustentabilidade ao decidir onde plantar suas florestas. Em alguns casos as decisões resultam em impactos negativos nos ecossistemas e comunidades locais. Além disso, com base no conhecimento da autora, não existem estudos que tratem das decisões de localização das florestas plantadas brasileiras e de todas essas três dimensões da sustentabilidade. Por tanto, este estudo tem como objetivo melhorar a compreensão do contexto atual de tomada de decisões em que as empresas na indústria brasileira de árvores plantadas escolhem os locais para suas florestas plantadas, e como os aspectos de sustentabilidade são incluídos em tais decisões. Como parte desse empenho, são examinadas possíveis limitações e outros aspectos de tomada de decisão, bem como o papel que as empresas atribuem à certos mecanismos para o desenvolvimento sustentável, como a política brasileira, a legislação e a certificação florestal. Uma estratégia de pesquisa qualitativa é adotada para conduzir a pesquisa. Uma revisão de literatura é combinada com entrevistas semiestruturadas com profissionais em dois segmentos da indústria brasileira de árvores plantadas. Suas percepções são trianguladas com perspectivas de atores relevantes de fora da indústria. A amostra de participantes do estudo foi realizada no Brasil entre 2015 e 2016 e resultou em 13 participantes. As entrevistas foram então realizadas em diversos locais no Brasil em 2016. É utilizada a teoria fundamentada nos dados como método de pesquisa para analisar os dados qualitativos coletados. São extraídos dois temas principais, diversos temas secundários e um conceito a partir dos dados qualitativos coletados, baseados principalmente nas percepções das empresas brasileiras de papel e celulose. Vários fatores de decisão que podem influenciar a localização das plantações florestais também são identificados. Observa-se que os participantes da amostra do estudo percebem que perspectivas estratégicas, econômicas, ambientais e sociais estão presentes nas decisões atuais de localização, realizadas por empresas certificadas de celulose e papel. Uma importante limitação do estudo é que sua característica exploratória impede que a pesquisadora apresente “verdades” sobre o assunto investigado, ou tire conclusões sobre os atos das empresas. Portanto, são apresentadas várias hipóteses aplicáveis ao Brasil, mas também uma hipótese de caráter geral. A hipótese geral é que as plantações florestais podem estar sujeitas a análises integradas de localização e sustentabilidade, onde o problema pode ser formulado como encontrar um local ideal para uma plantação florestal a partir de uma perspectiva tridimensional de sustentabilidade. Dadas as limitações do estudo, bem como as incertezas associadas às quais as hipóteses do Brasil estão relacionadas, a recomendação é que novos estudos se concentrem primeiro em testar a hipótese geral. Caso novos estudos em nível nacional forem benéficos após esses testes, os pesquisadores são incentivados a retornar ao resumo das perspectivas da indústria, das questões remanescentes e das hipóteses disponibilizadas para futuras pesquisas sobre o Brasil por meio deste estudo.
Klinka, Karel, Pal Varga, and Christine Chourmouzis. "Select CD : computer support system for making tree species and reproduction cutting decisions in the coastal forest of BC." Forest Sciences Department, University of British Columbia, 1999. http://hdl.handle.net/2429/672.
Full textBogdan, Vukobratović. "Hardware Acceleration of Nonincremental Algorithms for the Induction of Decision Trees and Decision Tree Ensembles." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=102520&source=NDLTD&language=en.
Full textУ овоj дисертациjи, представљени су нови алгоритми EFTI и EEFTI заформирање стабала одлуке и њихових ансамбала неинкременталномметодом, као и разне могућности за њихову имплементациjу.Експерименти показуjу да jе предложени EFTI алгоритам у могућностида произведе драстично мања стабла без губитка тачности у односу напостојеће top-down инкременталне алгоритме, а стабла знатно већетачности у односу на постојеће неинкременталне алгоритме. Такође супредложене хардверске архитектуре за акцелерацију ових алгоритама(EFTIP и EEFTIP) и показано је да је уз помоћ ових архитектура могућеостварити знатна убрзања.
U ovoj disertaciji, predstavljeni su novi algoritmi EFTI i EEFTI zaformiranje stabala odluke i njihovih ansambala neinkrementalnommetodom, kao i razne mogućnosti za njihovu implementaciju.Eksperimenti pokazuju da je predloženi EFTI algoritam u mogućnostida proizvede drastično manja stabla bez gubitka tačnosti u odnosu napostojeće top-down inkrementalne algoritme, a stabla znatno većetačnosti u odnosu na postojeće neinkrementalne algoritme. Takođe supredložene hardverske arhitekture za akceleraciju ovih algoritama(EFTIP i EEFTIP) i pokazano je da je uz pomoć ovih arhitektura mogućeostvariti znatna ubrzanja.
Wickramarachchi, Darshana Chitraka. "Oblique decision trees in transformed spaces." Thesis, University of Canterbury. Mathematics and Statistics, 2015. http://hdl.handle.net/10092/11051.
Full textShi, Haijian. "Best-first Decision Tree Learning." The University of Waikato, 2007. http://hdl.handle.net/10289/2317.
Full textVella, Alan. "Hyper-heuristic decision tree induction." Thesis, Heriot-Watt University, 2012. http://hdl.handle.net/10399/2540.
Full textHari, Vijaya. "Empirical Investigation of CART and Decision Tree Extraction from Neural Networks." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1235676338.
Full textAhmad, Amir. "Data Transformation for Decision Tree Ensembles." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508528.
Full textCai, Jingfeng. "Decision Tree Pruning Using Expert Knowledge." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1158279616.
Full textQureshi, Taimur. "Contributions to decision tree based learning." Thesis, Lyon 2, 2010. http://www.theses.fr/2010LYO20051/document.
Full textLa recherche avancée dans les méthodes d'acquisition de données ainsi que les méthodes de stockage et les technologies d'apprentissage, s'attaquent défi d'automatiser de manière systématique les techniques d'apprentissage de données en vue d'extraire des connaissances valides et utilisables.La procédure de découverte de connaissances s'effectue selon les étapes suivants: la sélection des données, la préparation de ces données, leurs transformation, le fouille de données et finalement l'interprétation et validation des résultats trouvés. Dans ce travail de thèse, nous avons développé des techniques qui contribuent à la préparation et la transformation des données ainsi qu'a des méthodes de fouille des données pour extraire les connaissances. A travers ces travaux, on a essayé d'améliorer l'exactitude de la prédiction durant tout le processus d'apprentissage. Les travaux de cette thèse se basent sur les arbres de décision. On a alors introduit plusieurs approches de prétraitement et des techniques de transformation; comme le discrétisation, le partitionnement flou et la réduction des dimensions afin d'améliorer les performances des arbres de décision. Cependant, ces techniques peuvent être utilisées dans d'autres méthodes d'apprentissage comme la discrétisation qui peut être utilisées pour la classification bayesienne.Dans le processus de fouille de données, la phase de préparation de données occupe généralement 80 percent du temps. En autre, elle est critique pour la qualité de la modélisation. La discrétisation des attributs continus demeure ainsi un problème très important qui affecte la précision, la complexité, la variance et la compréhension des modèles d'induction. Dans cette thèse, nous avons proposes et développé des techniques qui ce basent sur le ré-échantillonnage. Nous avons également étudié d'autres alternatives comme le partitionnement flou pour une induction floue des arbres de décision. Ainsi la logique floue est incorporée dans le processus d'induction pour augmenter la précision des modèles et réduire la variance, en maintenant l'interprétabilité.Finalement, nous adoptons un schéma d'apprentissage topologique qui vise à effectuer une réduction de dimensions non-linéaire. Nous modifions une technique d'apprentissage à base de variété topologiques `manifolds' pour savoir si on peut augmenter la précision et l'interprétabilité de la classification
Books on the topic "Tree of decisions"
Reed, W. J. Planting decisions in the face of uncertainty. Vancouver, B.C: Forest Economics and Policy Analysis Research Unit, University of British Columbia, 1991.
Find full textAlsolami, Fawaz, Mohammad Azad, Igor Chikalov, and Mikhail Moshkov. Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-12854-8.
Full textGladwin, Christina. Ethnographic decision tree modeling. Newbury Park: Sage, 1989.
Find full textGladwin, Christina H. Ethnographic decision tree modeling. Newbury Park: Sage, 1989.
Find full textKen, Friedman. The decision tree: A novel. Rainier, Wash: Heart Pub., 1996.
Find full textEuler, Bryan L. EDDT: Emotional Disturbance Decision Tree. Lutz, FL: Psychological Assessment Resources, 2007.
Find full textKustra, Rafal. Soft decision trees. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1999.
Find full textGrąbczewski, Krzysztof. Meta-Learning in Decision Tree Induction. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00960-5.
Full textAuthority, Financial Services. Stakeholder pensions decision trees. London: Financial Services Authority, 2000.
Find full textAssociation, American Bankers. Analyzing financial statements: A decision tree approach. Washington, D.C: American Bankers Association, 2013.
Find full textBook chapters on the topic "Tree of decisions"
Liu, Yangyang, Jiucheng Xu, Lin Sun, and Lina Du. "Decisions Tree Learning Method Based on Three-Way Decisions." In Lecture Notes in Computer Science, 389–400. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25783-9_35.
Full textSilva, Andreia, and Cláudia Antunes. "Pushing Constraints into a Pattern-Tree." In Modeling Decisions for Artificial Intelligence, 139–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41550-0_13.
Full textRodrigues, José F., Agma J. M. Traina, and Caetano Traina. "Visualization Tree, Multiple Linked Analytical Decisions." In Smart Graphics, 65–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11536482_6.
Full textXu, Tao, Yun Zhou, Alexander Raake, and Xuyun Zhang. "Analyzing Impact Factors for Smartphone Sharing Decisions Using Decision Tree." In Lecture Notes in Computer Science, 628–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91244-8_48.
Full textHofmockel, Kirsten, Curtis J. Richardson, and Patrick N. Halpin. "Effects of Hydrologic Management Decisions on Everglades Tree Islands." In Everglades Experiments, 191–214. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-68923-4_8.
Full textvan Ommen, Thijs, Wouter M. Koolen, and Peter D. Grünwald. "Efficient Algorithms for Minimax Decisions Under Tree-Structured Incompleteness." In Lecture Notes in Computer Science, 336–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29765-7_28.
Full textGustafsson, Janne, Ahti Salo, and Tommi Gustafsson. "PRIME Decisions: An Interactive Tool for Value Tree Analysis." In Lecture Notes in Economics and Mathematical Systems, 165–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56680-6_15.
Full textKatagiri, Hideki, Tomohiro Hayashida, Ichiro Nishizaki, and Jun Ishimatsu. "A Hybrid Algorithm Based on Tabu Search and Ant Colony Optimization for k-Minimum Spanning Tree Problems." In Modeling Decisions for Artificial Intelligence, 315–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04820-3_29.
Full textLee, Dong-Liang, Lawrence Y. Deng, Kung-Huang Lin, You-Syun Jheng, Yung-Hui Chen, Chih-Yang Chao, and Jiung-Yao Huang. "Using Decision Tree Analysis for Personality to Decisions of the National Skills Competition Participants." In Lecture Notes in Electrical Engineering, 683–91. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6996-0_72.
Full textCarroll, J. Douglas, and Geert De Soete. "Fitting a Quasi-Poisson Case of the GSTUN (General Stochastic Tree UNfolding) Model and Some Extensions." In Knowledge, Data and Computer-Assisted Decisions, 93–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84218-4_7.
Full textConference papers on the topic "Tree of decisions"
Huang, Sieh-Chuen, Hsuan-Lei Shao, and Robert B. Leflar. "Applying decision tree analysis to family court decisions." In ICAIL '21: Eighteenth International Conference for Artificial Intelligence and Law. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3462757.3466076.
Full textLerner, Scott, and Baris Taskin. "Towards Design Decisions for Genetic Algorithms in Clock Tree Synthesis." In 2018 Ninth International Green and Sustainable Computing Conference (IGSC). IEEE, 2018. http://dx.doi.org/10.1109/igcc.2018.8752170.
Full textRego, Paulo A. L., Elaine Cheong, Emanuel F. Coutinho, Fernando A. M. Trinta, Masum Z. Hasan, and Jose N. de Souza. "Decision Tree-Based Approaches for Handling Offloading Decisions and Performing Adaptive Monitoring in MCC Systems." In 2017 5th IEEE International Conference on Mobile Cloud Computing, Services and Engineering (MobileCloud). IEEE, 2017. http://dx.doi.org/10.1109/mobilecloud.2017.19.
Full textColes, Garill A., and Michael D. Zentner. "Application of Event Tree/Fault Tree Modeling Approach to the Evaluation of Proliferation Resistance." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43100.
Full textSu, BaoHe. "A Tree-based Concept Drift Detection Method by Three-way Decisions." In 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/amcce-17.2017.28.
Full textBrati Favarin, Samuel, and Rafael Ballottin Martins. "Aplicação de Mineração de Dados para o Auxílio da Tomada de Decisão em Gestão de Pessoas." In Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p028-030.
Full textRanaweera, Nesha, Amila Jayasinghe, and Chethika Abenayake. "Decision tree application for model built-up land fragmentation in urban areas." In ERU Symposium 2021. Engineering Research Unit (ERU), University of Moratuwa, 2021. http://dx.doi.org/10.31705/eru.2021.1.
Full textZhou, Ruihua. "Research on Investment Decisions of Open-ended Funds Based on Decision Tree, RF and LGBM during COVID-19." In 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022). Paris, France: Atlantis Press, 2022. http://dx.doi.org/10.2991/aebmr.k.220405.024.
Full textSullivan, G. J., and R. L. Baker. "Rate-distortion optimization for tree-structured source coding with multi-way node decisions." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226193.
Full textVan Bossuyt, Douglas, Chris Hoyle, Irem Y. Tumer, Andy Dong, Toni Doolen, and Richard Malak. "Toward Considering Risk Attitudes in Engineering Organizations Using Utility Theory." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70399.
Full textReports on the topic "Tree of decisions"
Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.
Full textHamilton, Jill, and Tuan Nguyen. Asbestos Inspection/Reinspection Decision Tree. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada370454.
Full textLee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.
Full textNarlikar, Girija J. A Parallel, Multithreaded Decision Tree Builder. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada363531.
Full textQuiller, Ryan. Decision Tree Technique for Particle Identification. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/815649.
Full textMughal, Mohamed. Biological Weapons Response Template and Decision Tree. Fort Belvoir, VA: Defense Technical Information Center, April 2001. http://dx.doi.org/10.21236/ada385897.
Full textDakin, Gordon, and Sankar Virdhagriswaran. Misleading Information Detection Through Probabilistic Decision Tree Classifiers. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada406823.
Full textKwon, Theresa Hyunjin, Erin Cho, and Youn-Kyung Kim. Identifying Sustainable Style Consumers with Decision Tree Predictive Model. Ames: Iowa State University, Digital Repository, November 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1366.
Full textEccleston, C. H. The decision - identification tree: A new EIS scoping tool. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/16876.
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