Littérature scientifique sur le sujet « Interpretable coefficients »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Interpretable coefficients ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Interpretable coefficients"
Lubiński, Wojciech, et Tomasz Gólczewski. « Physiologically interpretable prediction equations for spirometric indexes ». Journal of Applied Physiology 108, no 5 (mai 2010) : 1440–46. http://dx.doi.org/10.1152/japplphysiol.01211.2009.
Texte intégralLIPOVETSKY, STAN. « MEANINGFUL REGRESSION COEFFICIENTS BUILT BY DATA GRADIENTS ». Advances in Adaptive Data Analysis 02, no 04 (octobre 2010) : 451–62. http://dx.doi.org/10.1142/s1793536910000574.
Texte intégralLawless, Connor, Jayant Kalagnanam, Lam M. Nguyen, Dzung Phan et Chandra Reddy. « Interpretable Clustering via Multi-Polytope Machines ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 7 (28 juin 2022) : 7309–16. http://dx.doi.org/10.1609/aaai.v36i7.20693.
Texte intégralEshima, Nobuoki, Claudio Giovanni Borroni, Minoru Tabata et Takeshi Kurosawa. « An Entropy-Based Tool to Help the Interpretation of Common-Factor Spaces in Factor Analysis ». Entropy 23, no 2 (24 janvier 2021) : 140. http://dx.doi.org/10.3390/e23020140.
Texte intégralLiu, Jin, Robert A. Perera, Le Kang, Roy T. Sabo et Robert M. Kirkpatrick. « Obtaining Interpretable Parameters From Reparameterized Longitudinal Models : Transformation Matrices Between Growth Factors in Two Parameter Spaces ». Journal of Educational and Behavioral Statistics 47, no 2 (1 décembre 2021) : 167–201. http://dx.doi.org/10.3102/10769986211052009.
Texte intégralTakada, Masaaki, Taiji Suzuki et Hironori Fujisawa. « Independently Interpretable Lasso for Generalized Linear Models ». Neural Computation 32, no 6 (juin 2020) : 1168–221. http://dx.doi.org/10.1162/neco_a_01279.
Texte intégralBazilevskiy, Mikhail Pavlovich. « Program for Constructing Quite Interpretable Elementary and Non-elementary Quasi-linear Regression Models ». Proceedings of the Institute for System Programming of the RAS 35, no 4 (2023) : 129–44. http://dx.doi.org/10.15514/ispras-2023-35(4)-7.
Texte intégralYeung, Michael. « Attention U-Net ensemble for interpretable polyp and instrument segmentation ». Nordic Machine Intelligence 1, no 1 (1 novembre 2021) : 47–49. http://dx.doi.org/10.5617/nmi.9157.
Texte intégralBarnett, Tim, et Patricia A. Lanier. « Comparison of Alternative Response Formats for an Abbreviated Version of Rotter's Locus of Control Scale ». Psychological Reports 77, no 1 (août 1995) : 259–64. http://dx.doi.org/10.2466/pr0.1995.77.1.259.
Texte intégralZheng, Fanglan, Erihe, Kun Li, Jiang Tian et Xiaojia Xiang. « A federated interpretable scorecard and its application in credit scoring ». International Journal of Financial Engineering 08, no 03 (6 août 2021) : 2142009. http://dx.doi.org/10.1142/s2424786321420093.
Texte intégralThèses sur le sujet "Interpretable coefficients"
Gnanguenon, guesse Girault. « Modélisation et visualisation des liens entre cinétiques de variables agro-environnementales et qualité des produits dans une approche parcimonieuse et structurée ». Electronic Thesis or Diss., Montpellier, 2021. http://www.theses.fr/2021MONTS139.
Texte intégralThe development of digital agriculture allows to observe at high frequency the dynamics of production according to the climate. Data from these dynamic observations can be considered as functional data. To analyze this new type of data, it is necessary to extend the usual statistical tools to the functional case or develop new ones.In this thesis, we have proposed a new approach (SpiceFP: Sparse and Structured Procedure to Identify Combined Effects of Functional Predictors) to explain the variations of a scalar response variable by two or three functional predictors in a context of joint influence of these predictors. Particular attention was paid to the interpretability of the results through the use of combined interval classes defining a partition of the observation domain of the explanatory factors. Recent developments around LASSO (Least Absolute Shrinkage and Selection Operator) models have been adapted to estimate the areas of influence in the partition via a generalized penalized regression. The approach also integrates a double selection, of models (among the possible partitions) and of variables (areas inside a given partition) based on AIC and BIC information criteria. The methodological description of the approach, its study through simulations as well as a case study based on real data have been presented in chapter 2 of this thesis.The real data used in this thesis were obtained from a vineyard experiment aimed at understanding the impact of climate change on anthcyanins accumulation in berries. Analysis of these data in chapter 3 using SpiceFP and one extension identified a negative impact of morning combinations of low irradiance (lower than about 100 µmol/s/m2 or 45 µmol/s/m2 depending on the advanced-delayed state of the berries) and high temperature (higher than about 25°C). A slight difference associated with overnight temperature occurred between these effects identified in the morning.In chapter 4 of this thesis, we propose an implementation of the proposed approach as an R package. This implementation provides a set of functions allowing to build the class intervals according to linear or logarithmic scales, to transform the functional predictors using the joint class intervals and finally to execute the approach in two or three dimensions. Other functions help to perform post-processing or allow the user to explore other models than those selected by the approach, such as an average of different models.Keywords: Penalized regressions, Interaction, information criteria, scalar-on-function, interpretable coefficients,grapevine microclimate
FICCADENTI, Valerio. « A rank-size approach to the analysis of socio-economics data ». Doctoral thesis, 2018. http://hdl.handle.net/11393/251181.
Texte intégralChapitres de livres sur le sujet "Interpretable coefficients"
Sohns, J. T., D. Gond, F. Jirasek, H. Hasse, G. H. Weber et H. Leitte. « Embedding-Space Explanations of Learned Mixture Behavior ». Dans Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes, 32–50. Cham : Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35779-4_3.
Texte intégralTurbé, Hugues, Mina Bjelogrlic, Mehdi Namdar, Christophe Gaudet-Blavignac, Jamil Zaghir, Jean-Philippe Goldman, Belinda Lokaj et Christian Lovis. « A Lightweight and Interpretable Model to Classify Bundle Branch Blocks from ECG Signals ». Dans Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220393.
Texte intégralActes de conférences sur le sujet "Interpretable coefficients"
Zhang, R., G. S. Li, X. Z. Yao, J. G. Shi, Y. Guo, X. Z. Song, Z. P. Zhu et B. Y. Li. « An Interpretable Method for Formation Pressure Calculation with Embedding Mechanism ». Dans 57th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/arma-2023-0094.
Texte intégralChen, Zhi-Xuan, Cheng Jin, Tian-Jing Zhang, Xiao Wu et Liang-Jian Deng. « SpanConv : A New Convolution via Spanning Kernel Space for Lightweight Pansharpening ». Dans 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.2022/118.
Texte intégralTang, Tianning, Haoyu Ding, Saishuai Dai, Xi Chen, Paul H. Taylor, Jun Zang et Thomas A. A. Adcock. « Data Informed Model Test Design With Machine Learning – An Example in Nonlinear Wave Load on a Vertical Cylinder ». Dans ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/omae2023-102682.
Texte intégralOmer, Pareekhan. « Improving Prediction Accuracy of Lasso and Ridge Regression as an Alternative to LS Regression to Identify Variable Selection Problems ». Dans 3rd International Conference of Mathematics and its Applications. Salahaddin University-Erbil, 2020. http://dx.doi.org/10.31972/ticma22.05.
Texte intégralWu, Jingyao, Ting Dang, Vidhyasaharan Sethu et Eliathamby Ambikairajah. « Belief Mismatch Coefficient (BMC) : A Novel Interpretable Measure of Prediction Accuracy for Ambiguous Emotion States ». Dans 2023 11th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2023. http://dx.doi.org/10.1109/acii59096.2023.10388210.
Texte intégralShao, Puheng, Zhenwu Fang, Jinxiang Wang, Zhongsheng Lin et Guodong Yin. « Modeling and Explanation of Driver Steering Style : An Experiment under Large-Curvature Road Condition ». Dans Human Systems Engineering and Design (IHSED 2021) Future Trends and Applications. AHFE International, 2021. http://dx.doi.org/10.54941/ahfe1001208.
Texte intégral