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Статті в журналах з теми "Black Box trees"
Veugen, Thijs, Bart Kamphorst, and Michiel Marcus. "Privacy-Preserving Contrastive Explanations with Local Foil Trees." Cryptography 6, no. 4 (October 28, 2022): 54. http://dx.doi.org/10.3390/cryptography6040054.
Повний текст джерелаStone, C., and PE Bacon. "Influence of Insect Herbivory on the Decline of Black Box (Eucalyptus largiflorens)." Australian Journal of Botany 43, no. 6 (1995): 555. http://dx.doi.org/10.1071/bt9950555.
Повний текст джерелаMcTavish, Hayden, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, and Margo Seltzer. "Fast Sparse Decision Tree Optimization via Reference Ensembles." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 9604–13. http://dx.doi.org/10.1609/aaai.v36i9.21194.
Повний текст джерелаWelchowski, Thomas, Kelly O. Maloney, Richard Mitchell, and Matthias Schmid. "Techniques to Improve Ecological Interpretability of Black-Box Machine Learning Models." Journal of Agricultural, Biological and Environmental Statistics 27, no. 1 (October 28, 2021): 175–97. http://dx.doi.org/10.1007/s13253-021-00479-7.
Повний текст джерелаBarbosa, Pedro, Astrid Caldas, and Gaden Robinson. "Host Plant Associations among Species in Two Macrolepidopteran Assemblages." Journal of Entomological Science 38, no. 1 (January 1, 2003): 41–47. http://dx.doi.org/10.18474/0749-8004-38.1.41.
Повний текст джерелаWagers, Steven, Guillermo Castilla, Michelle Filiatrault, and G. Arturo Sanchez-Azofeifa. "Using TLS-Measured Tree Attributes to Estimate Aboveground Biomass in Small Black Spruce Trees." Forests 12, no. 11 (November 4, 2021): 1521. http://dx.doi.org/10.3390/f12111521.
Повний текст джерелаShahpouri, Saeid, Armin Norouzi, Christopher Hayduk, Reza Rezaei, Mahdi Shahbakhti, and Charles Robert Koch. "Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines." Energies 14, no. 23 (November 24, 2021): 7865. http://dx.doi.org/10.3390/en14237865.
Повний текст джерелаWongvibulsin, Shannon, Katherine C. Wu, and Scott L. Zeger. "Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation." JMIR Medical Informatics 8, no. 6 (June 9, 2020): e15791. http://dx.doi.org/10.2196/15791.
Повний текст джерелаFernando, Denise R., Jonathan P. Lynch, Meredith T. Hanlon, and Alan T. Marshall. "Foliar elemental microprobe data and leaf anatomical traits consistent with drought tolerance in Eucalyptus largiflorens (Myrtaceae)." Australian Journal of Botany 69, no. 4 (2021): 215. http://dx.doi.org/10.1071/bt20170.
Повний текст джерелаDuryea, Mary, George Blakeslee, William Hubbard, and Ricardo Vasquez. "Wind and Trees: A Survey of Homeowners After Hurricane Andrew." Arboriculture & Urban Forestry 22, no. 1 (January 1, 1996): 44–50. http://dx.doi.org/10.48044/jauf.1996.006.
Повний текст джерелаДисертації з теми "Black Box trees"
Saeed, Umar, and Ansur Mahmood Amjad. "ISTQB : Black Box testing Strategies used in Financial Industry for Functional testing." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3237.
Повний текст джерелаKamal, Ahmad Waqas. "A Hierarchical Approach to Software Testing." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4889.
Повний текст джерелаBensadon, Jérémy. "Applications de la théorie de l'information à l'apprentissage statistique." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS025/document.
Повний текст джерелаWe study two different topics, using insight from information theory in both cases: 1) Context Tree Weighting is a text compression algorithm that efficiently computes the Bayesian combination of all visible Markov models: we build a "context tree", with deeper nodes corresponding to more complex models, and the mixture is computed recursively, starting with the leaves. We extend this idea to a more general context, also encompassing density estimation and regression; and we investigate the benefits of replacing regular Bayesian inference with switch distributions, which put a prior on sequences of models instead of models. 2) Information Geometric Optimization (IGO) is a general framework for black box optimization that recovers several state of the art algorithms, such as CMA-ES and xNES. The initial problem is transferred to a Riemannian manifold, yielding parametrization-invariant first order differential equation. However, since in practice, time is discretized, this invariance only holds up to first order. We introduce the Geodesic IGO (GIGO) update, which uses this Riemannian manifold structure to define a fully parametrization invariant algorithm. Thanks to Noether's theorem, we obtain a first order differential equation satisfied by the geodesics of the statistical manifold of Gaussians, thus allowing to compute the corresponding GIGO update. Finally, we show that while GIGO and xNES are different in general, it is possible to define a new "almost parametrization-invariant" algorithm, Blockwise GIGO, that recovers xNES from abstract principles
Dubois, Amaury. "Optimisation et apprentissage de modèles biologiques : application à lirrigation [sic l'irrigation] de pomme de terre." Thesis, Littoral, 2020. http://www.theses.fr/2020DUNK0560.
Повний текст джерелаThe subject of this PhD concerns one of the LISIC themes : modelling and simulation of complex systems, as well as optimization and automatic learning for agronomy. The objectives of the thesis are to answer the questions of irrigation management of the potato crop and the development of decision support tools for farmers. The choice of this crop is motivated by its important share in the Haut-de-France region. The manuscript is divided into 3 parts. The first part deals with continuous multimodal optimization in a black box context. This is followed by a presentation of a methodology for the automatic calibration of biological model parameters through reformulation into a black box multimodal optimization problem. The relevance of the use of inverse analysis as a methodology for automatic parameterisation of large models in then demonstrated. The second part presents 2 new algorithms, UCB Random with Decreasing Step-size and UCT Random with Decreasing Step-size. Thes algorithms are designed for continuous multimodal black-box optimization whose choice of the position of the initial local search is assisted by a reinforcement learning algorithms. The results show that these algorithms have better performance than (Quasi) Random with Decreasing Step-size algorithms. Finally, the last part focuses on machine learning principles and methods. A reformulation of the problem of predicting soil water content at one-week intervals into a supervised learning problem has enabled the development of a new decision support tool to respond to the problem of crop management
Harland, A. N. "Tracing local hydrology and water source use of Eucalyptus largiflorens on the Calperum Floodplain using strontium, oxygen and deuterium isotopes." Thesis, 2018. http://hdl.handle.net/2440/130626.
Повний текст джерелаBlack Box trees (Eucalyptus largiflorens) across the Murray-Darling Basin are in critical condition due to high groundwater salinity and infrequent natural flooding. Geochemical tracers such as radiogenic strontium (87Sr/86Sr), oxygen-18 (𝛿𝛿18O) and deuterium (𝛿𝛿D) are considered useful in the understanding of catchment hydrology and plant water use, and in this study, 87Sr/86Sr, 𝛿𝛿18O and 𝛿𝛿D isotopes were used accordingly to better comprehend local hydrology and water use behaviour patterns of Black Box trees on the Calperum Floodplain, South Australia. Investigations were achieved by sampling and analysing local surface waters (Lake Merreti, Lake Clover, and River Murray), groundwater, soils (1.5 m depth) and plant material (stem water, and leaves) from two separate sites, north (Site 1) and south (Site 4). Considering the local hydrology, Lake Clover was composed of evaporated rainwater, while Lake Merreti was a relative mix of both evaporated rainwater and river water. Additionally, local rainfall sources appeared to vary overtime. Furthermore, groundwater showed no close relationship with rain water suggesting an alternative recharge source such as river water or remnant paleo-water. In terms of water use, linear mixing models using soil 87Sr/86Sr, leaf 87Sr/86Sr and stem water 𝛿𝛿18O inputs showed that Site 1 trees, on average, were predominately using rainwater (77%, 77% & 67%), while Site 4 trees used both rainwater (16%, 32% & 42%) and saline groundwater (70%, 62% & 58%), regardless of nearby lakes and streams. These findings have implications for future monitoring, and the management of outer floodplain Black Box populations that are unable to receive natural flooding inundation.
Thesis (B.Sc.(Hons)) -- University of Adelaide, School of Physical Sciences, 2018
Salvaire, Pierre Antony Jean Marie. "Explaining the predictions of a boosted tree algorithm : application to credit scoring." Master's thesis, 2019. http://hdl.handle.net/10362/85991.
Повний текст джерелаThe main goal of this report is to contribute to the adoption of complex « Black Box » machine learning models in the field of credit scoring for retail credit. Although numerous investigations have been showing the potential benefits of using complex models, we identified the lack of interpretability as one of the main vector preventing from a full and trustworthy adoption of these new modeling techniques. Intrinsically linked with recent data concerns such as individual rights for explanation, fairness (introduced in the GDPR1) or model reliability, we believe that this kind of research is crucial for easing its adoption among credit risk practitioners. We build a standard Linear Scorecard model along with a more advanced algorithm called Extreme Gradient Boosting (XGBoost) on a retail credit open source dataset. The modeling scenario is a binary classification task consisting in identifying clients that will experienced 90 days past due delinquency state or worse. The interpretation of the Scorecard model is performed using the raw output of the algorithm while more complex data perturbation technique, namely Partial Dependence Plots and Shapley Additive Explanations methods are computed for the XGBoost algorithm. As a result, we observe that the XGBoost algorithm is statistically more performant at distinguishing “bad” from “good” clients. Additionally, we show that the global interpretation of the XGBoost is not as accurate as the Scorecard algorithm. At an individual level however (for each instance of the dataset), we show that the level of interpretability is very similar as they are both able to quantify the contribution of each variable to the predicted risk of a specific application.
Частини книг з теми "Black Box trees"
Greenwell, Brandon M. "Peeking inside the “black box”: post-hoc interpretability." In Tree-Based Methods for Statistical Learning in R, 203–28. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003089032-6.
Повний текст джерелаLampridis, Orestis, Riccardo Guidotti, and Salvatore Ruggieri. "Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars." In Discovery Science, 357–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_24.
Повний текст джерела"Operator Control Parameters and Fine Tuning of Genetic Algorithms (GAs)." In Advances in Computational Intelligence and Robotics, 115–27. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4105-0.ch007.
Повний текст джерелаLoute, Alain. "The “Pragmatist Turn” in Theory of Governance." In Ethical Governance of Emerging Technologies Development, 213–20. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3670-5.ch014.
Повний текст джерелаZou, Jinying, and Ovanes Petrosian. "Explainable AI: Using Shapley Value to Explain Complex Anomaly Detection ML-Based Systems." In Machine Learning and Artificial Intelligence. IOS Press, 2020. http://dx.doi.org/10.3233/faia200777.
Повний текст джерелаLorbiecki, Marybeth. "The Endangered Species and Youth: Keeping All the Cogs and Wheels." In A Fierce Green Fire. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780199965038.003.0025.
Повний текст джерелаТези доповідей конференцій з теми "Black Box trees"
Martignon, Laura, Joachim Engel, and Tim Erickson. "A Transparent, Simple AI Tool for Constructing Efficient and Robust Fast and Frugal Trees for Classification Under Risk." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t6g3.
Повний текст джерелаDann, Michael, Yuan Yao, Brian Logan, and John Thangarajah. "Multi-Agent Intention Progression with Black-Box Agents." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/19.
Повний текст джерелаSantos, Samara Silva, Marcos Antonio Alves, Leonardo Augusto Ferreira, and Frederico Gadelha Guimarães. "PDTX: A novel local explainer based on the Perceptron Decision Tree." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-50.
Повний текст джерелаAllen, Cameron, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, and Gerald Tesauro. "Efficient Black-Box Planning Using Macro-Actions with Focused Effects." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/554.
Повний текст джерелаLaget, Hannes, Michae¨l Deneve, Evert Vanderhaegen, and Thomas Museur. "Combustion Dynamics Data Mining Techniques: A Way to Gain Enhanced Insight in the Combustion Processes of Fielded Gas Turbines." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59553.
Повний текст джерелаOliveira-Junior, Robinson A. A. de. "Credit scoring development in the light of the new Brazilian General Data Protection Law." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/kdmile.2021.17462.
Повний текст джерелаGarifullin, Albert, Alexandr Shcherbakov, and Vladimir Frolov. "Fitting Parameters for Procedural Plant Generation." In WSCG'2022 - 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2022. Západočeská univerzita, 2022. http://dx.doi.org/10.24132/csrn.3201.35.
Повний текст джерелаDudek, Jeffrey M., Aditya A. Shrotri, and Moshe Y. Vardi. "DPSampler: Exact Weighted Sampling Using Dynamic Programming." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/250.
Повний текст джерелаRahutomo, S. "Shallow Water Subsea Well Drilling and Completion Utilizing Jack Up Rig at Natuna Sea Block." In Indonesian Petroleum Association 44th Annual Convention and Exhibition. Indonesian Petroleum Association, 2021. http://dx.doi.org/10.29118/ipa21-e-38.
Повний текст джерелаIllich, Moritz, and Birte Glimm. "Computing Concept Referring Expressions for Queries on Horn ALC Ontologies." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/370.
Повний текст джерелаЗвіти організацій з теми "Black Box trees"
Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, January 2023. http://dx.doi.org/10.26509/frbc-wp-202305.
Повний текст джерела