Relevant bibliographies by topics / Tree training

Contents

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

Academic literature on the topic 'Tree training'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Tree training"

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Graehl, Jonathan, Kevin Knight, and Jonathan May. "Training Tree Transducers." Computational Linguistics 34, no. 3 (2008): 391–427. http://dx.doi.org/10.1162/coli.2008.07-051-r2-03-57.

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Many probabilistic models for natural language are now written in terms of hierarchical tree structure. Tree-based modeling still lacks many of the standard tools taken for granted in (finite-state) string-based modeling. The theory of tree transducer automata provides a possible framework to draw on, as it has been worked out in an extensive literature. We motivate the use of tree transducers for natural language and address the training problem for probabilistic tree-to-tree and tree-to-string transducers.
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Blackburn, Bryan, and Curt R. Rom. "Early Performance of Six Peach Training Systems." HortScience 33, no. 4 (1998): 600a—600. http://dx.doi.org/10.21273/hortsci.33.4.600a.

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The effects of six freestanding training systems (Open Center, Untrained, 2-Scaffold V, 4-Scaffold V, Leaning V, and Central Leader at tree densities of 161, 161, 245, 375, 375, and 300 trees/acre, respectively) on yield and tree growth of `Redhaven' on Lovell rootstock were evaluated. Open-center and untrained trees were largest and had greatest yields per tree. The 2-scaffold V had the greatest production in kilograms per acre. Early productivity was related to tree density and pruning severity, not tree size. Training systems had no effect on fruit size.
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Churn, Peter. "The tree of training." Education for Primary Care 30, no. 4 (2019): 243–45. http://dx.doi.org/10.1080/14739879.2019.1613934.

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Evans, G. E., D. E. Deyton, and J. W. High. "COMPARISONS OF PLANT DENSITIES AND TRAINING SYSTEMS OF PEACH TREES." HortScience 27, no. 6 (1992): 638d—638. http://dx.doi.org/10.21273/hortsci.27.6.638d.

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`Redhaven' peach tree plantings were established in 1985 to compare tree densities (299 trees/ha to 1794 trees/ha) and training systems (Open Vase, Central Leader, Y-shaped, Palmette Trellis, Tatura Trellis, and MIA Trellis). Tree trunk growth (diameter) was significantly less as the population of trees increased. Trunks of trees trained to the Open Vase were larger than Central Leader or Y-shaped trees. In 1988, yields per ha increased as tree density increased. Trees trained to the Tatura Trellis (897 trees/ha) had the highest yields (27.7 t/ha). Trees trained to the Central Leader and plant
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Blažková, J., H. Drahošová, and I. Hlušičková. "Tree vigour, cropping, and phenology of sweet cherries in two systems of tree training on dwarf rootstocks." Horticultural Science 37, No. 4 (2010): 127–38. http://dx.doi.org/10.17221/60/2010-hortsci.

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Five cultivars and four rootstocks (Gisela 5, P-HL-A, P-HL-B, and Tabel Edabriz) were evaluated on trees in fruiting stage trained like spindle and on trellis. Tree vigour was significantly related to cultivar, rootstock and to tree training. Spindles were generally more vigorous than trees on trellis with exception of cv. Kordia. In several cases special combinations of cultivar, rootstock, and method of tree training differed significantly from mean effects of the three factors. Time of flowering was significantly dependent on the cultivar and varied annually within 15 days. Time of fruit ha
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Ozkan, Y., K. Yıldız, E. Küçüker, Ç. Çekiç, M. Özgen, and Y. Akça. " Early performance of cv. Jonagold apple on M.9 in five tree training systems." Horticultural Science 39, No. 4 (2012): 158–63. http://dx.doi.org/10.17221/35/2012-hortsci.

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The effects of five training systems on tree growth, fruit yield and some fruit characteristics were assessed in Jonagold apple cv. grafted on M.9 rootstock. The trees were trained in one of five ways: slender spindle (SS; 4,761 trees/ha), vertical axis (VA; 2,857 trees/ha), hytec (HT; 1,904 tree/ha) and two different tree densities of super spindle (L-Super S with 5,000 trees/ha; H-Super S with 10,000 trees/ha). Trunk cross-sectional area (TCA) was higher in HT and VA than SS, L-Super S and H-Super S in the 4<sup>th</sup> year. While HT had the highest cumulative yield/tree, the l
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Connell, Joseph H., Warren Micke, James Yeager, Janine Hasey, Bill Krueger, and Craig Weakley. "TRAINING PERMANENT AND TEMPORARY TREES." HortScience 25, no. 9 (1990): 1169c—1169. http://dx.doi.org/10.21273/hortsci.25.9.1169c.

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High orchard establishment costs require greater production early in an orchard's life. Our goal was to develop temporary trees at the least cost with the best early production. Health and longevity of permanent trees is essential. Six pruning treatments were evaluated in five-tree plots using a randomized complete block design. Each treatment was replicated four times on the `Butte' and `Mission' almond cultivars. After six years, temporary trees receiving the least pruning had the highest yields. Permanent trees had lower yields since more pruning was done in the second through fourth dorman
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McFadyen, Lisa, David Robertson, Stephen Morris, and Trevor Olesen. "Effects of Early Tree Training on Macadamia Production." HortTechnology 26, no. 6 (2016): 707–12. http://dx.doi.org/10.21273/horttech03479-16.

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The current industry recommendation for the training of young macadamia (Macadamia integrifolia, Macadamia tetraphylla, and hybrids) trees is to prune the trees to a central leader, but there is little science to support this recommendation. We planted an orchard to assess the merits of central leader training relative to a minimally pruned control. We used two cultivars, 246 and 816, representing spreading and upright growth habits, respectively. Training to a central leader reduced cumulative yields per tree over the first 3 years of production by 16% in ‘246’ and 23% in ‘816’. The reduction
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Campbell, JE, HI Nicol, and BR Cullis. "Effect of four different canopy shapes on apple yields." Australian Journal of Experimental Agriculture 36, no. 4 (1996): 489. http://dx.doi.org/10.1071/ea9960489.

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The cultivars, Jonathan, Delicious and Granny Smith apple (Malus domestica Borkh.) trees on MM.106, Northern Spy, Seedling and MM.102 rootstocks were trained as-vase, central leader, palmette and Hawkes Bay multi-leade; systems for 18 years. Rootstock significantly affected tree size, and there were interactions of rootstock with training systems or cultivars. There was also an interaction between training ' systems and cultivars. In the early years, while the training systems were being established, fruit yield was inversely related to the severity of the pruning; central leader- and palmette
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Fallahi, Esmaeil, Bahar Fallahi, and Shahla Mahdavi. "Branch Configuration Impacts on Production, Fruit Quality, and Leaf Minerals of ‘Aztec Fuji’ Apple Trees in an Upright Single Row High-Density Orchard System Over Five Years." Journal of Agricultural Science 12, no. 4 (2020): 53. http://dx.doi.org/10.5539/jas.v12n4p53.

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Tree architectures play a critical role in the productivity of high-density orchards, but limited information is available in this subject. We studied effects of three branch configurations on tree growth, yield components, fruit quality and leaf mineral nutrients in ‘Aztec Fuji’ apple (Malus domestica Bork.) in a single row upright high-density system under southwest Idaho, USA conditions over 2012-2016. This study revealed that trees trained into a Tall Spindle (TS) had larger trunk cross sectional area (TCSA) than those with an Overlapped Arm (OA) system. Trees trained i
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Dissertations / Theses on the topic "Tree training"

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Teichmann, Markus. "Expressing Context-Free Tree Languages by Regular Tree Grammars." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-224756.

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In this thesis, three methods are investigated to express context-free tree languages by regular tree grammars. The first method is a characterization. We show restrictions to context-free tree grammars such that, for each restricted context-free tree grammar, a regular tree grammar can be constructed that induces the same tree language. The other two methods are approximations. An arbitrary context-free tree language can be approximated by a regular tree grammar with a restricted pushdown storage. Furthermore, we approximate weighted context-free tree languages, induced by weighted linear non
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Gerber, Hein Jaco. "Tree training and managing complexity and yield in fig (Ficus carica L.)." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/3184.

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Thesis (MscAgric (Horticulture))--University of Stellenbosch, 2010.<br>ENGLISH ABSTRACT: Commercial fig production with popular European cultivars, Bourjasotte Noire, Col de Damme Noire and Noire de Caromb, is new to the Western Cape. Little research on fig production has been conducted in South Africa and producers are struggling to implement effective commercial practices. In order to establish practices that will maximise yield of quality fruit, the most productive one-year-old shoot lengths were identified in a phenological study. All shoot length categories evaluated in ‘Bourjasotte Noir
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Mesa, Juliani Karen <1982&gt. "Starch distribution in pear tree organs in relation to training systems, rootstocks and fruit quality." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/7158/.

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Starch is the main form in which plants store carbohydrates reserves, both in terms of amounts and distribution among different plant species. Carbohydrates are direct products of photosynthetic activity, and it is well know that yield efficiency and production are directly correlated to the amount of carbohydrates synthesized and how these are distributed among vegetative and reproductive organs. Nowadays, in pear trees, due to the modernization of orchards, through the introduction of new rootstocks and the development of new training systems, the understanding and the development of new app
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DeGomez, Tom. "Training and Pruning Newly Planted Decidous Fruit Trees." College of Agriculture, University of Arizona (Tucson, AZ), 2015. http://hdl.handle.net/10150/559564.

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6 pp.<br>Training and pruning newly-planted deciduous fruit trees is one of the most important steps in developing trees with a strong framework (scaffold branches). Trees with a good framework of branches can support heavy crops without limb breakage and will help to bring the young tree into production at an early age. Selection and arrangement of these branches determines the type of development and growth in later years. The goal of pruning and training is to balance vegetative and fruiting wood growth.
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Belzer, Jessica A. "Unmanned Aircraft Systems in the National Airspace System: Establishing Equivalencyin Safety and Training Through a Fault Tree Analysis Approach." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1492778505498031.

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Prince, Jared. "Exploring the Effect of Different Numbers of Convolutional Filters and Training Loops on the Performance of AlphaZero." TopSCHOLAR®, 2018. https://digitalcommons.wku.edu/theses/3087.

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In this work, the algorithm used by AlphaZero is adapted for dots and boxes, a two-player game. This algorithm is explored using different numbers of convolutional filters and training loops, in order to better understand the effect these parameters have on the learning of the player. Different board sizes are also tested to compare these parameters in relation to game complexity. AlphaZero originated as a Go player using an algorithm which combines Monte Carlo tree search and convolutional neural networks. This novel approach, integrating a reinforcement learning method previously applied to
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Dietze, Toni [Verfasser], Heiko [Akademischer Betreuer] Vogler, and Johanna [Gutachter] Björklund. "A Formal View on Training of Weighted Tree Automata by Likelihood-Driven State Splitting and Merging / Toni Dietze ; Gutachter: Johanna Björklund ; Betreuer: Heiko Vogler." Dresden : Technische Universität Dresden, 2019. http://d-nb.info/1226899870/34.

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Xia, Tian. "Learning to Rank Algorithms and Their Application in Machine Translation." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1451610555.

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Davison, Elisabeth, and Tom DeGomez. "Pruning Deciduous Shade Trees." College of Agriculture, University of Arizona (Tucson, AZ), 2015. http://hdl.handle.net/10150/560971.

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Revised; Originally published:1999<br>6 pp.<br>The pruning principles discussed in this publication have proven to provide the best possible out comes including tree longevity and safety. Although trees may live for years following improper pruning their life span and safety may be severely reduced. We encourage proper pruning so that the trees we care for may bring us pleasure for many years.
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Castellanos, de Varona Maria Elena. "Cross-cultural training program." Theological Research Exchange Network (TREN), access this title online, 2001. http://dx.doi.org/10.2986/tren.108-0017.

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Books on the topic "Tree training"

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Berges, Amanda J. Training tree for riders. Alpine Publications, 2012.

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Wick, John. Walk with dick: The tree dog encyclopedia. John Wick, 1985.

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Wick, John. Walk with Wick: The tree dog encyclopedia. Westwick Pub., 2003.

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Jepson, Jeff. The tree climber's companion: A compact field reference and training manuel [sic] for tree climbers. J. Jepson, 1997.

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Johansson, Kicki. Planting trees: An illustrated technical guide and training manual. United Nations Development Programme, 1993.

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Edwards, D. G. W. A training guide for laboratory analysis of forest tree seeds. Pacific Forestry Centre, 1995.

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FAO/DANIDA Training Course on Forest Tree Improvement (1980 Mérida, Venezuela). Forest tree improvement: Report on the FAO/DANIDA Training Course on Forest Tree Improvement ; Mérida, Venezuela, January - February 1980. Food and Agriculture Organization of the United Nations, 1985.

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The illustrated practical encyclopedia of pruning, training & topiary: How to prune and train trees, shrubs, hedges, topiary, tree and soft fruit, climbers and roses. Lorenz, 2005.

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Nelson, William R. Response tree evaluation: Experimental assessment of an expert system for nuclear reactor operators. EG&G Idaho, 1985.

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United States. President (1989-1993 : Bush). Proposed legislation: "National Tree Trust Act of 1990" : message from the President of the United States transmitting a legislative proposal to authorize the President to designate a private nonprofit Foundation as eligible to receive funds for the purpose of promoting community tree planting and cultivation projects ... to establish a rural tree planting program and a community tree planting program, and for other purposes. U.S. G.P.O., 1990.

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Book chapters on the topic "Tree training"

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Asadi, Nima, and Jimmy Lin. "Training Efficient Tree-Based Models for Document Ranking." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36973-5_13.

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López De Luise, M. D., M. Soffer, and F. Spelanzon. "Improved Induction Tree Training for Automatic Lexical Categorization." In Advanced Techniques in Computing Sciences and Software Engineering. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3660-5_9.

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Cervantes, Jair, Asdrúbal López, Farid García, and Adrián Trueba. "A Fast SVM Training Algorithm Based on a Decision Tree Data Filter." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25324-9_16.

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Appice, Annalisa, Sonja Pravilovic, Donato Malerba, and Antonietta Lanza. "Sampling Training Data for Accurate Hyperspectral Image Classification via Tree-Based Spatial Clustering." In AI*IA 2017 Advances in Artificial Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70169-1_23.

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Sutton-Charani, Nicolas, Sébastien Destercke, and Thierry Denœux. "Training and Evaluating Classifiers from Evidential Data: Application to E2M Decision Tree Pruning." In Belief Functions: Theory and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11191-9_10.

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Liu, Haozhe, Hongzhan Lin, and Guang Chen. "TANTP: Conversational Emotion Recognition Using Tree-Based Attention Networks with Transformer Pre-training." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75765-6_58.

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Fang, Huang, Minhao Cheng, Cho-Jui Hsieh, and Michael Friedlander. "Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization." In Proceedings of the 2019 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2019. http://dx.doi.org/10.1137/1.9781611975673.32.

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Winter, Jerrold. "The Training Effect." In True Nutrition, True Fitness. Humana Press, 1991. http://dx.doi.org/10.1007/978-1-4612-0479-4_19.

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Cao, Qing-qing, and Wen-juan Huai. "Safety Risk Assessment and Management of Military Physical Fitness Training Based on Triangular Fuzzy Mathematics and Fault Tree Analysis." In IE&EM 2019. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4530-6_10.

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Benenson, Efim. "Diagnostic Training and Rheumatology Trees." In Rheumatology. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-240-7_1.

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Conference papers on the topic "Tree training"

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Karapinar, Zehra, Arafat Senturk, Sultan Zavrak, Resul Kara, and Pakize Erdogmus. "Binary apple tree: A game approach to tree traversal algorithms." In 2012 11th International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE, 2012. http://dx.doi.org/10.1109/ithet.2012.6246026.

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Dalponte, Michele, Liviu Theodor Ene, Hans Ole Orka, Terje Gobakken, and Erik Naesset. "Unsupervised selection of training plots and trees for tree species classification." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723225.

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Wen, Zeyi, Bingsheng He, Ramamohanarao Kotagiri, Shengliang Lu, and Jiashuai Shi. "Efficient Gradient Boosted Decision Tree Training on GPUs." In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2018. http://dx.doi.org/10.1109/ipdps.2018.00033.

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Wong, Jeremy H. M., and Mark J. F. Gales. "Student-Teacher Training with Diverse Decision Tree Ensembles." In Interspeech 2017. ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-145.

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Dietterich, Thomas G., Adam Ashenfelter, and Yaroslav Bulatov. "Training conditional random fields via gradient tree boosting." In Twenty-first international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1015330.1015428.

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Choudhury, Rituparna, S. R. Ahamed, and Prithwijit Guha. "Efficient Hardware Implementation of Decision Tree Training Accelerator." In 2020 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS). IEEE, 2020. http://dx.doi.org/10.1109/ises50453.2020.00055.

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Robert, Ornprapa P., Chamnan Kumsap, and Sibsan Suksuchano. "Modeling realistic 3D trees using materials from field survey for terrain analysis of tactical training center." In THE 9TH INTERNATIONAL DEFENCE AND HOMELAND SECURITY SIMULATION WORKSHOP. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.dhss.006.

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This paper elaborates processes of modeling 3D trees for the simulation of the Army’s Tactical Training Center. The ultimate objective is to develop the 3D model database for inclusion to a game engine library. The adopted methodology includes collecting a forestry inventory for later 3D tree modeling in a Unity’s 3D Tree Modeler. Leaves and trunks were closely modeled using the data collected from the real site in the package SpeedTree modeler. Three tree types were sampled to demonstrate how close and realistic the adopted processes were to produce result 3D models for inclusion to the simul
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Sadasue, Tamon, and Tsuyoshi Isshiki. "Scalable Full Hardware Logic Architecture for Gradient Boosted Tree Training." In 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 2020. http://dx.doi.org/10.1109/fccm48280.2020.00067.

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Ouzounis, G. K., and L. Gueguen. "Interactive collection of training samples from the Max-Tree structure." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6115714.

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Kazama, Jun'ichi, and Kentaro Torisawa. "Speeding up training with tree kernels for node relation labeling." In the conference. Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1220575.1220593.

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Reports on the topic "Tree training"

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Bonner, F. T., James A. Vozzo, W. W. Elam, and S. B. Land. Tree Seed Technology Training Course. U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1994. http://dx.doi.org/10.2737/so-gtr-107.

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Bonner, F. T., John A. Vozzo, W. W. Elam, and S. B. Land. Tree Seed Technology Training Course - Instructor's Manual. U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1994. http://dx.doi.org/10.2737/so-gtr-106.

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Abate, Gashaw Tadesse, Tanguy Bernard, Mekdim D. Regassa, and Bart Minten. Improving coffee productivity in Ethiopia: The impact of a coffee tree rejuvenation training program on stumping. International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134408.

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PARSONS ENGINEERING SCIENCE INC DENVER CO. Treatability Study in Support of the Intrinsic Remediation Option at the Christmas Tree Fire Training Area at Westover Air Reserve Base Chicopee, Massachusetts. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada343849.

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Contreras Salamanca, Luz Briyid, and Yon Garzón Ávila. Generational Lagging of Dignitaries, Main Cause of Technological Gaps in Community Leaders. Analysis of Generation X and Boomers from the Technology Acceptance Model. Universidad Nacional Abierta y a Distancia, 2021. http://dx.doi.org/10.22490/ecacen.4709.

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Community and neighborhood organizations are in the process of renewing the organizational culture, considering technological environments in the way of training, and advancing communally, being competitive in adaptation and learning, creating new solutions, promoting change, and altering the status quo, based on the advancement of technology over the last few years, currently applied in most organizations. The decisive factor is the ability of true leaders to appropriate the Technological Acceptance Model –TAM– principles, participating in programs and projects, adopting new technologies from
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Logger in training dies after being struck by a previously felled tree which rolled off of a bank in West Virginia. U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, 2003. http://dx.doi.org/10.26616/nioshsface02wv040.

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West Virginia dozer operator dies after being struck-by a tree which was pushed over by the worker he was training. U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, 2002. http://dx.doi.org/10.26616/nioshsface02wv009.

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