Rapidly exploring learning trees by Shiarlis, K, Messias, J, Whiteson, S

“…In this paper, we propose Rapidly Exploring Learning Trees (RLT∗ ), which learns the cost functions of Optimal Rapidly Exploring Random Trees (RRT∗ ) from demonstration, thereby making inverse learning methods applicable to more complex tasks. …”

Learning Tree Structured Potential Games by Garg, Vikas K., Jaakkola, Tommi

“…Many real phenomena, including behaviors, involve strategic interactions that can be learned from data. We focus on learning tree structured potential games where equilibria are represented by local maxima of an underlying potential function. …”
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Learning Tree Structured Potential Games by Garg, Vikas K., Jaakkola, Tommi

“…Many real phenomena, including behaviors, involve strategic interactions that can be learned from data. We focus on learning tree structured potential games where equilibria are represented by local maxima of an underlying potential function. …”
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The effectiveness of learning tree species through mobile augmented reality application / by Amirul Afiq Jamaluddin, 1996-, author, Noradila Rusli @ Ruslii, supervisor, Noordini Che'man supervisor, Fakulti Alam bina dan Ukur

The effectiveness of learning tree species through mobile augmented reality application / by Amirul Afiq Jamaluddin, 1996-, author

Large-deviation analysis and applications Of learning tree-structured graphical models by Tan, Vincent Yan Fu

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What Is a Healthy Forest? A Supplement to Florida Project Learning Tree by Sarah L. Hicks, Martha C. Monroe, Geetha S. Iyer, Jason A. Smith

“…A Supplement to Florida Project Learning Tree (ufl.edu) …”
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Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization by Solomon Asante-Okyere, Chuanbo Shen, Harrison Osei

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Resource Review: Project Learning Tree PreK-8 Environmental Education Activity Guide by Rachel Bayer

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Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests by Jamie Imada, Juan Carlos Arango-Sabogal, Cathy Bauman, Steven Roche, David Kelton

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Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction by Cong Wang, Lisha Zhao, Shuhong Wu, Xinmin Song

“…Here we introduce a new approach to reservoir surveillance that uses the machine learning tree boosting method to forecast production data. …”
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Addressing Sunshine State Standards in Elementary School Teacher Professional Development Workshops by Jennifer A. Seitz, Martha C. Monroe, Kimberly O. Thurman

“…Thurman, use Project Learning Tree (PLT) workshops as an example of how anyone conducting professional development workshops can improve attendance and enhance program use by public school teachers by identifying and advertising the Sunshine State Standards (SSS) that the workshops address. …”
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Improving Inservice Teacher Workshops in Florida by Martha C. Monroe, Jenny Seitz, Shruti Agrawal, Sheda Morshed, Elizabeth Swiman, Michelle Aldridge, Vicki Crisp

“…Monroe, Jenny Seitz, Shruti Agrawal, Sheda Morshed, Elizabeth Swiman, Michelle Aldridge, and Vicki Crisp, defines the terms that are most helpful for Florida environmental education workshop facilitators and provides suggestions for planning, advertising, delivering, and evaluating workshops to better meet the needs of our teachers. Project Learning Tree (PLT) workshops are used as an example of how nonformal education facilitators could adapt their workshops to better address the new professional development system. …”
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Improving Inservice Teacher Workshops in Florida by Martha C. Monroe, Jenny Seitz, Shruti Agrawal, Michelle Aldridge, Sheda Morshed, Elizabeth Swiman, Vicki Crisp

“…This fact sheet defines the terms that are most helpful and provides suggestions for planning, advertising, delivering, and evaluating workshops to meet the needs of our teachers. Project Learning Tree (PLT) workshops are used as an example of how nonformal education facilitators could adapt their workshops to better address the new professional development system. …”
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Reinforcement learning and A* search for the unit commitment problem by Patrick de Mars, Aidan O’Sullivan

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Detection of tree defects via ground penetrating radar by Dang, Thanh Nhan

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How can artificial intelligence and data science algorithms predict life expectancy - An empirical investigation spanning 193 countries by Akanmode Eyitayo Ronmi, Rajesh Prasad, Baku Agyo Raphael

“…Furthermore, four machine learning tree regression algorithms were employed using the identified attributes to develop and evaluate life expectancy prediction models. …”
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Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction by Ul Haq, Zeeshan, Ullah, Hafeez, Khan, Muhammad Nouman Aslam, Naqvi, Salman Raza, Abdul Ahad, Abdul Ahad, Saidina Amin, Nor Aishah

“…The results proposed that Ensembled Learning Tree (ELT-PSO) model outperformed all other models and is favored for biochar yield prediction (R2 = 0.99, RMSE = 2.33). …”

A Light Gradient-Boosting Machine algorithm with Tree-Structured Parzen Estimator for breast cancer diagnosis by Temidayo Oluwatosin Omotehinwa, David Opeoluwa Oyewola, Emmanuel Gbenga Dada

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Microservice-based scalable online platform for lifelongers by Nguyen, Dinh Hoang

“…Therefore, an online platform, a web application for lifelong learning which is “Learning Tree” would be an essential platform to provide people with such necessary learning opportunities. …”
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