Relevant bibliographies by topics / Leave-one-out cross validation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Leave-one-out cross validation'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Leave-one-out cross validation"

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Pronzato, Luc, and Maria-João Rendas. "Weighted Leave-One-Out Cross Validation." SIAM/ASA Journal on Uncertainty Quantification 12, no. 4 (2024): 1213–39. http://dx.doi.org/10.1137/23m1615917.

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Zhang, Tong. "Leave-One-Out Bounds for Kernel Methods." Neural Computation 15, no. 6 (2003): 1397–437. http://dx.doi.org/10.1162/089976603321780326.

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In this article, we study leave-one-out style cross-validation bounds for kernel methods. The essential element in our analysis is a bound on the parameter estimation stability for regularized kernel formulations. Using this result, we derive bounds on expected leave-one-out cross-validation errors, which lead to expected generalization bounds for various kernel algorithms. In addition, we also obtain variance bounds for leave-oneout errors. We apply our analysis to some classification and regression problems and compare them with previous results.
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Pauli, Martin Patrick, Constantin Pohl, and Martin Golz. "Balanced Leave-One-Subject-Out Cross- Validation for Microsleep Classification." Current Directions in Biomedical Engineering 7, no. 2 (2021): 147–50. http://dx.doi.org/10.1515/cdbme-2021-2038.

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Abstract Inter-individual differences in the feature distribution of electroencephalograms (EEG) during microsleep (MS) raise questions about the generalizability of the methodology, because methodology should have the same validity for subjects to be included in the analysis in the future as for subjects included so far. We address this question using leave-one-subject- out cross-validation (LOSO CV) to simulate inclusion of test data from future subjects. Investigations are based on EEG of 70 subjects across four studies conducted in our driving simulation lab. 9,297 MS and 10,264 counter-ex
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Gronau, Quentin F., and Eric-Jan Wagenmakers. "Rejoinder: More Limitations of Bayesian Leave-One-Out Cross-Validation." Computational Brain & Behavior 2, no. 1 (2019): 35–47. http://dx.doi.org/10.1007/s42113-018-0022-4.

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Homrighausen, Darren, and Daniel J. McDonald. "Leave-one-out cross-validation is risk consistent for lasso." Machine Learning 97, no. 1-2 (2014): 65–78. http://dx.doi.org/10.1007/s10994-014-5438-z.

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Lv, Liye, Xueguan Song, and Wei Sun. "Modify Leave-One-Out Cross Validation by Moving Validation Samples around Random Normal Distributions: Move-One-Away Cross Validation." Applied Sciences 10, no. 7 (2020): 2448. http://dx.doi.org/10.3390/app10072448.

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The leave-one-out cross validation (LOO-CV), which is a model-independent evaluate method, cannot always select the best of several models when the sample size is small. We modify the LOO-CV method by moving a validation point around random normal distributions—rather than leaving it out—naming it the move-one-away cross validation (MOA-CV), which is a model-dependent method. The key point of this method is to improve the accuracy rate of model selection that is unreliable in LOO-CV without enough samples. Errors from LOO-CV and MOA-CV, i.e., LOO-CVerror and MOA-CVerror, respectively, are empl
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Du, Dajun, Kang li, Minrui Fei, and George W. Irwin. "Automatic Forward Model Selection Based on Leave-One-Out Cross-Validation." IFAC Proceedings Volumes 42, no. 10 (2009): 874–79. http://dx.doi.org/10.3182/20090706-3-fr-2004.00145.

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Feng, Ziheng, Xianpeng Zong, Tianfa Xie, and Xinyu Zhang. "Kriging Model Averaging Based on Leave-One-Out Cross-Validation Method." Journal of Systems Science and Complexity 37, no. 5 (2024): 2132–56. http://dx.doi.org/10.1007/s11424-024-3150-z.

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Cawley, Gavin C., and Nicola L. C. Talbot. "Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers." Pattern Recognition 36, no. 11 (2003): 2585–92. http://dx.doi.org/10.1016/s0031-3203(03)00136-5.

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Shao, Zhifei, and Meng Joo Er. "Efficient Leave-One-Out Cross-Validation-based Regularized Extreme Learning Machine." Neurocomputing 194 (June 2016): 260–70. http://dx.doi.org/10.1016/j.neucom.2016.02.058.

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Dissertations / Theses on the topic "Leave-one-out cross validation"

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Tandan, Isabelle, and Erika Goteman. "Bank Customer Churn Prediction : A comparison between classification and evaluation methods." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-411918.

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This study aims to assess which supervised statistical learning method; random forest, logistic regression or K-nearest neighbor, that is the best at predicting banks customer churn. Additionally, the study evaluates which cross-validation set approach; k-Fold cross-validation or leave-one-out cross-validation that yields the most reliable results. Predicting customer churn has increased in popularity since new technology, regulation and changed demand has led to an increase in competition for banks. Thus, with greater reason, banks acknowledge the importance of maintaining their customer base
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Dizon, Lucas, and Martin Johansson. "Atrial Fibrillation Detection Algorithm Evaluation and Implementation in Java." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-158878.

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Atrial fibrillation is a common heart arrhythmia which is characterized by a missing or irregular contraction of the atria. The disease is a risk factor for other more serious diseases and the total medical costs in society are extensive. Therefore it would be beneficial to improve and optimize the prevention and detection of the disease.   Pulse palpation and heart auscultation can facilitate the detection of atrial fibrillation clinically, but the diagnosis is generally confirmed by an ECG examination. Today there are several algorithms that detect atrial fibrillation by analysing an ECG. A
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Book chapters on the topic "Leave-one-out cross validation"

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Webb, Geoffrey I., Claude Sammut, Claudia Perlich, et al. "Leave-One-Out Cross-Validation." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_469.

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Sreedharan, Radhika, Jigna Prajapati, Pinalkumar Engineer, and Deep Prajapati. "Leave-One-Out Cross-Validation in Machine Learning." In Ethical Issues in AI for Bioinformatics and Chemoinformatics. CRC Press, 2023. http://dx.doi.org/10.1201/9781003353751-5.

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Yuan, Jin, Yan-Ming Li, Cheng-Liang Liu, and Xuan F. Zha. "Leave-One-Out Cross-Validation Based Model Selection for Manifold Regularization." In Advances in Neural Networks - ISNN 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13278-0_59.

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Zhang, Tong. "A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44581-1_28.

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Kunjan, Sajeev, T. S. Grummett, K. J. Pope, et al. "The Necessity of Leave One Subject Out (LOSO) Cross Validation for EEG Disease Diagnosis." In Brain Informatics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86993-9_50.

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Igarashi, Hiroki, Nobuaki Yasuo, and Masakazu Sekijima. "Leave-One-Element-Out Cross-Validation for Band Gap Prediction of Halide Double Perovskites." In Advances in Parallel & Distributed Processing, and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69984-0_55.

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Kurogi, Shuichi, Tomokazu Nagi, Shoichi Yoshinaga, Hideaki Koya, and Takeshi Nishida. "Multiview Range Image Registration Using Competitive Associative Net and Leave-One-Image-Out Cross-Validation Error." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24965-5_70.

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Kurogi, Shuichi, Yoichiro Yamashita, Hikaru Yoshikawa, and Kotaro Hirayama. "Accuracy Improvement of Localization and Mapping of ICP-SLAM via Competitive Associative Nets and Leave-One-Out Cross-Validation." In Neural Information Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_20.

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"Leave-One-Out Cross-Validation." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_469.

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Vehtari, Aki, and Jouko Lampinen. "Expected Utility Estimation via Cross-Validation." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0050.

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Abstract We discuss practical methods for the assessment, comparison and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important to obtain the distribution of the expected utility estimate in order to describe the associated uncertainty. We synthesize and extend the previous work in several ways. We give a unified presentation from the Bayesian viewpoint emphasizing the assumptions made and propose practical meth
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Conference papers on the topic "Leave-one-out cross validation"

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Hamidi, A., K. Mohamed-Pour, and M. Yousefi. "Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation." In 2024 32nd International Conference on Electrical Engineering (ICEE). IEEE, 2024. http://dx.doi.org/10.1109/icee63041.2024.10667765.

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Lilly, Alexander, Steven Kopitzke, and Ian Long. "Development of Corrosion Severity Assessment Algorithms Using Environmental Monitoring Sensors in Naval Aviation Environments." In CONFERENCE 2024. AMPP, 2024. https://doi.org/10.5006/c2024-20663.

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Abstract Currently, Naval aircraft are inspected for corrosion based on Reliability Centered Maintenance analysis of component failure rates in a feedback loop scheme. Recently developed sensors allow for the measurement and recording of temperature, relative humidity, and solution conductivity in 30 minute intervals. These sensors were deployed collocated with corrosion witness coupons across two 1-year deployments spanning 26 naval aviation relevant locations. Features were engineered from the sensor data, and algorithms were trained to predict the mass loss observed on the collocated witnes
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Rayo, Lautaro, and Ibrahim Hoteit. "Ensemble Kalman filter regularization using leave-one-out data cross-validation." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756379.

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Kearns, Michael, and Dana Ron. "Algorithmic stability and sanity-check bounds for leave-one-out cross-validation." In the tenth annual conference. ACM Press, 1997. http://dx.doi.org/10.1145/267460.267491.

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Cawley, G. C. "Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246634.

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Masayuki Karasuyama and Ryohei Nakano. "Optimizing Sparse Kernel Ridge Regression hyperparameters based on leave-one-out cross-validation." In 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong). IEEE, 2008. http://dx.doi.org/10.1109/ijcnn.2008.4634291.

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Morozov, Alexey, Brian Angulo, Vadim Mottl, Alexander Tatarchuk, and Olga Krasotkina. "Differential Leave-One-Out Cross-Validation for Feature Selection in Generalized Linear Dependence Models." In ITCC 2021: 2021 3rd International Conference on Information Technology and Computer Communications. ACM, 2021. http://dx.doi.org/10.1145/3473465.3473474.

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Morozov, Alexey, Brian Angulo, Vadim Mottl, Alexander Tatarchuk, and Olga Krasotkina. "Differential Leave-One-Out Cross-Validation for Feature Selection in Generalized Linear Dependence Models." In ITCC 2021: 2021 3rd International Conference on Information Technology and Computer Communications. ACM, 2021. http://dx.doi.org/10.1145/3473465.3473474.

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Holden, Sean B. "PAC-like upper bounds for the sample complexity of leave-one-out cross-validation." In the ninth annual conference. ACM Press, 1996. http://dx.doi.org/10.1145/238061.238067.

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Sari, Mayang, and Wikky Fawwaz Al Maki. "Improving K-Nearest Neighbor Performance in Footwear Classification Using Leave One Out Cross Validation." In 2023 3rd International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2023. http://dx.doi.org/10.1109/icicyta60173.2023.10428849.

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