Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation

Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.

Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949

Untitled work

(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909

A Probabilistic Theory of Pattern Recognition

Devroye, L., Györfi, L., & Lugosi, G. (1996) · Stochastic Modelling and Applied Probability · doi:10.1007/978-1-4612-0711-5 · Cited by 3,274

Estimation of Dependences Based on Empirical Data

Vapnik, V. N. (2006) · Information science and statistics · doi:10.1007/0-387-34239-7 · Cited by 2,252

Subset Selection in Regression

Miller, A. J. (2003) · Technometrics · doi:10.1198/tech.2003.s144 · Cited by 1,501

Decision theoretic generalizations of the PAC model for neural net and other learning applications

Haussler, D. H. (1992) · Information and Computation · doi:10.1016/0890-5401(92)90010-d · Cited by 845

Subset Selection in Regression.

RAAB, G. M., & Miller, T. (1991) · Biometrics · doi:10.2307/2532418 · Cited by 547

Statistical mechanics of learning from examples

Seung, H. S., Sompolinsky, H., & Tishby, N. (1992) · Physical Review A · doi:10.1103/physreva.45.6056 · Cited by 532

Toward efficient agnostic learning

Kearns, M. J., Schapire, R. E., & Sellie, L. M. (1992) · Conference on Learning Theory · doi:10.1145/130385.130424 · Cited by 353

Tightening the Sample Complexity of Empirical Risk Minimization via Preconditioned Stability.Preprint

Gonen, A., & Shalev‐Shwartz, S. (2016) · arXiv (Cornell University) · Cited by 4

Handleap: towards contact-free gesture interaction with earphones via acoustic sensing

He, Y., Jin, Y., & Jin, Z. (2025) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-024-00180-x · Cited by 3

Individual identification for different age groups using functional connectivity strength

Zhang, Y., Liu, S., & Yu, X. (2019) · Neurological Sciences · doi:10.1007/s10072-019-04109-6 · Cited by 3

Stability and Generalization of lp-Regularized Stochastic Learning for GCN

Liu, S., Wei, L., Lv, S., & Li, M. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/631 · Cited by 3

Generalization bounds for learning under graph-dependence: a survey

Zhang, R.-R., & Amini, M.-R. (2024) · Machine Learning · doi:10.1007/s10994-024-06536-9 · Cited by 3

Development of an Enterprise-Grade Contract Understanding System

Agarwal, A. K., Chiticariu, L., Raman, P. C., Danilevsky, M., Ghazi, D., Gupta, A., Guttula, S., Katsis, Y., Krishnamurthy, R., Li, Y., et al. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-industry.28 · Cited by 3

Energy-efficient EEG monitoring systems for wireless epileptic seizure detection

Hussein, R., & Ward, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819045-6.00004-2 · Cited by 3

Insect classification using Scanning Electron Microphotographs considering magnifications

Takahashi, A., Ogawa, T., & Haseyama, M. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738673 · Cited by 3

A Vision System for Recognizing Objects in Complex Real Images

Daliri, M. R., Vanzella, W., & Torre, V. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-76856-2_23 · Cited by 3

A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

Sun, D., Ling, Z., Wang, S., Qiu, Z., Huan, Y., Mao, Z., & He, Y. (2019) · Applied Sciences · doi:10.3390/app10010023 · Cited by 3

Adaptive Classifiers in Stationary Conditions

Alippi, C., & Roveri, M. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371096 · Cited by 3

An Exponential Efron-Stein Inequality for Lq Stable Learning RulesPreprint

Abou–Moustafa, K. T., & Szepesvári, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.05457 · Cited by 3

Enabling Reliable Visual Detection of Chronic Myocardial Infarction with Native T1 Cardiac MRI Using Data-Driven Native Contrast Mapping

Youssef, K., Zhang, X., Yoosefian, G., Chen, Y., Chan, S. F., Yang, H., Vora, K. P., Howarth, A. G., Kumar, A., Sharif, B., et al. (2024) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.230338 · Cited by 2

Application of the Polynomial-Based Least Squares and Total Least Squares Models for the Attenuated Total Reflection Fourier Transform Infrared Spectra of Binary Mixtures of Hydroxyl Compounds

Shan, P., Peng, S., Zhao, Y., & Tang, L. (2016) · Applied Spectroscopy · doi:10.1177/0003702815626680 · Cited by 2

LINEAR AND NONLINEAR ASSOCIATION MEASURES WITH INTRADAY/HIGH FREQUENCY DATA FOR ALL IBOVESPA STOCKS

Block, A. S., Ceretta, P. S., & da Costa, A. S. (2016) · Revista Eletrônica de Estratégia & Negócios · doi:10.19177/reen.v8e32015171-186 · Cited by 2

Median-Plane Head-Related Transfer Function Personalization Using Two-Dimensional Independent Component Analysis

Liu, X., Huang, W., Zhang, H., & Zhong, X. (2022) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc56324.2022.10065739 · Cited by 2

The Bounds on the Rate of Uniform Convergence for Learning Machine

Zou, B., Li, L., & Xu, J. (2005) · Lecture notes in computer science · doi:10.1007/11427391_86 · Cited by 2

Ultrasound-based computer navigation: An accurate measurement tool for determining combined anteversion?

Wassilew, G. I., Hasart, O., Heller, M. O. W., Perka, C., Südhoff, I., Janz, V., Seeger, J. B., & König, C. (2012) · Technology and Health Care · doi:10.3233/thc-2012-00699 · Cited by 2

On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers

Rubinstein, B. I. P., & Simma, A. (2012) · IEEE Transactions on Information Theory · doi:10.1109/tit.2012.2191681 · Cited by 2

k-NN classifiers: Investigating the k=k(n) relationship

Alippi, C., Fuhrman, M., & Roveri, M. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634324 · Cited by 2

On the Flexibility of Theoretical Models for Pattern Recognition

Ryabko, D. (2005) · Cited by 2

The epidural needle guidance with an intelligent and automatic identification system for epidural anesthesia

Kao, M.-C., Kuo, W., & Ting, C. (2018) · Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems · doi:10.1117/12.2289750 · Cited by 2

An Exponential Tail Bound for the Deleted Estimate

Abou–Moustafa, K. T., & Szepesvári, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013143 · Cited by 2

A novel sleep/wake identification method with video analysis

Wang, Y.-K., Chen, H., Chen, J.-R., Lin, C.-M., & Chiu, H. (2013) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2013.6890761 · Cited by 2

Automatic role recognition

Salamin, H. (2013) · Enlighten: Theses (The University of Glasgow) · Cited by 2

A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning

Zhang, T. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44581-1_5 · Cited by 2

Signal fluctuations induced by non‐T1‐related confounds in variable TR fMRI experiments

Hu, S., Olulade, O. A., Tamer, G. G., Luh, W., & Talavage, T. M. (2009) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21767 · Cited by 2

An a Priori Exponential Tail Bound for k-Folds Cross-ValidationPreprint

Abou–Moustafa, K. T., & Szepesvári, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05801 · Cited by 2

Concentration inequalities of the cross-validation estimator for Empirical Risk MinimiserPreprint

Cornec, M. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1011.0096 · Cited by 2

Rademacher upper bounds for cross-validation errors with an application to the lassoPreprint

Xu, N., Fisher, T. C. G., & Hong, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.15598 · Cited by 2

The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated LearningPreprint

Jafarigol, E., & Trafali̇s, T. B. (2023) · Research Square · doi:10.21203/rs.3.rs-3492014/v1 · Cited by 4

Adversarial Robustness

He, F., & Tao, D. (2025) · Machine learning · doi:10.1007/978-981-16-8233-9_13 · Cited by 1

Explainable AI for SQL Grading: A Practical Approach with Multi-task CNNs

Rivas, P., & Schwartz, D. R. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-86623-4_5 · Cited by 1

Application of Improved Three-Dimensional Kernel Approach to Prediction of Protein Structural Class

Liu, X., Zhang, Y., Yang, H., Wang, L., & Liu, S. (2013) · BioMed Research International · doi:10.1155/2013/625403 · Cited by 1

The generalization performance of learning algorithms derived simultaneously through algorithmic stability and space complexity

Xu, J., & Zou, B. (2011) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2011.6022044 · Cited by 1

Measurement of auditory hemodynamic response function due to different temporal patterns of imaging acoustic noise using functional magnetic resonance imaging

Ranaweera, R. D., Kwon, M., Hu, S., Tamer, G. G., & Talavage, T. M. (2011) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciinfs.2011.6038073 · Cited by 1

Decoding underlying brain activities in time and frequency domains through complex independent component analysis of EEG signals

Valenza, G., Vanello, N., Milanesi, M., Scilingo, E. P., & Landini, L. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944301 · Cited by 1

RFE-SVM for discrimination of Pericarpium Citri Reticulatae through chromatography

Zhang, Y., Li, L., Wu, K., Wei, H., Li, X., Li, Z., Qi, L., & Chen, Q. (2016) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2016.7872997 · Cited by 1

An Exponential Tail Bound for Lq Stable Learning Rules. Application to k-Folds Cross-Validation.

Abou–Moustafa, K. T., & Szepesvári, C. (2018) · ISAIM · Cited by 1

Background and Notation

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.002 · Cited by 1

Experimental Evidences on Healthy Subjects and Bipolar Patients

Valenza, G., & Scilingo, E. P. (2013) · Series in bioengineering · doi:10.1007/978-3-319-02639-8_5 · Cited by 1

A PAC Bound for Approximate Support Vector Machines

Cao, D., & Boley, D. L. (2007) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972771.44 · Cited by 1

Classifying Insects from SEM Images Based on Optimal Classifier Selection and D-S Evidence Theory

Ogawa, T., Takahashi, A., & HASEYAMA, M. (2016) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.e99.a.1971 · Cited by 1

Earth Pressure Multipoint Prediction for EPS Shield Based on Multi-Model Ensemble

Zhang, Z., Liu, Z., Su, H., Mao, W., & Ma, L. (2018) · Chinese Automation Congress · doi:10.1109/cac.2018.8623330 · Cited by 1

Discriminative Analysis of Depression Patients Studied with Structural MR Images Using Support Vector Machine and Recursive Feature Elimination

Wang, J., Peng, H., Zhang, Y., & Wu, K. (2019) · Sensing and Imaging · doi:10.1007/s11220-019-0242-2 · Cited by 1

AI Memo AIM-2003-019 Permutation Tests for Classication

Mukherjee, S., Golland, P., & Panchenko, D. (2003) · Cited by 1

A Nonconformity Approach to Model Selection for SVMsPreprint

Hardoon, D. R., Hussain, Z., & Shawe-Taylor, J. S. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0909.2332 · Cited by 1

New upper bounds on cross-validation for the k-Nearest Neighbor classification rulePreprint

Célisse, A., & Mary‐Huard, T. (2015) · arXiv (Cornell University) · Cited by 1

Combining unsupervised and supervised machine learning to build user models for intelligent learning environments

Amershi, S. (2011) · cIRcle (University of British Columbia) · doi:10.14288/1.0052078 · Cited by 1

APPRENABILITÉ DANS LES PROBLÈMES DE L'INFÉRENCE SÉQUENTIELLE

Ryabko, D. (2011) · LillOA (Université de Lille (University Of Lille)) · Cited by 1

Machine Learning Search for Suitable Structure Directing Agents for the Synthesis of Beta ( BEA ) Zeolite Using Molecular Topology and Monte Carlo Techniques

Gálvez-Llompart, M., & Sastre, G. (2023) · doi:10.1002/9781119819783.ch3 · Cited by 1

Estimators of extra-sample error for non- parametric methods. A comparison based on extensive simulations.

Borra, S., & Ciaccio, A. D. (2008) · Cineca Institutional Research Information System (Tor Vergata University) · Cited by 1

Optymalizacja statystycznych systemów uczących się

Jankowski, S. (2012) · Prace Naukowe Politechniki Warszawskiej. Elektronika

Probabilistic Connection between Cross-Validation and Vapnik Bounds

Klęsk, P. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-29966-7_3

The Study of Leave-One-Out Error-Based Classification Learning Algorithm for Generalization Performance

Zou, B., Xu, J., & Li, L. (2006) · Lecture notes in computer science · doi:10.1007/11881070_2

Analysis of forearm muscles activity by means of new protocols of multichannel EMG signal recording and processing

Martínez, M. R. (2012) · doi:10.5821/dissertation-2117-94974

A framework for classification with single feature kernel matrix

Huang, H., Zuo, X., Huang, C. B., & Liu, J. (2013) · Optik · doi:10.1016/j.ijleo.2013.07.104

Assessing and selecting gene expression signals based upon the quality of the measured dynamics

Yang, E., & Androulakis, I. P. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-55

Variable Selection in Proteomic Profile Classification by Interval Support Vector Machines (iSVM)

Yang, X. L., & He, H. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.556-562.347

Reliable nearest neighbors for lazy learning

Ebert, T., Kampmann, G., & Nelles, O. (2011) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2011.5991139

Semi-analytical method for analyzing models and model selection measures

Dobra, A., & Dhurandhar, A. (2009)

Centroid Estimation of Leading Target Vehicle Based on Decision Trees

Dai, X., Kummert, A., Park, S.-B., & Iurgel, U. (2008) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2008-01-1256

Prediction Analysis of Artificial Landscape Water Eutrophication

Ai, J., Xu, H., Cai, Y., Wu, S., & Lei, Z. (2016) · International Conference on Education, Sports, Arts and Management Engineering (ICESAME) · doi:10.2991/icesame-16.2016.230

Some Notes on the Stability of Learning

Deng, Y., Guo, J. H., & Luo, S. (2006) · International Conference on Natural Language Processing and Knowledge Engineering (NLPKE) · doi:10.1109/nlpke.2005.1598837

Experimental Applications on Multi-Sensory Affective Stimulation

Greco, A., Valenza, G., & Scilingo, E. P. (2016) · Springer eBooks · doi:10.1007/978-3-319-46705-4_5

Model based process design for a monoclonal antibody-producing cell line : optimisation using hybrid modelling and an agent based system

Green, A. J. (2015)

Land Cover Dependent Error in Intermap IFSAR DTM

Coveney, S. (2013) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.79.3.277

Structure-based Subfamily Classification of Homeodomains

Tsai, J. (2008) · TSpace (University of Toronto)

The strong convergence of visual classification method and its applications in disease diagnosis

Meng, D., Xu, Z., Leung, Y., & Fung, T. (2017) · Figshare · doi:10.26180/5619484

Using Magnetoencephalography to Monitor the Progression from Mild Cognitive Impairment to Alzheimer Disease : An Approach Based on Resting State Functional Connectivity

Pusil, S. A. (2018) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.50766

The “Five W” of MS and EE

Oneto, L. (2019) · Modeling and optimization in science and technologies · doi:10.1007/978-3-030-24359-3_2

Adversarial Machine Learning Challenges

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.009

A Framework for Secure Learning

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.003

The “Ground-Glass” Mimicker in The Pandemic: A Novel Radiomics-Based Machine Learning Model Differentiates COVID-19 Pneumonia from Acute Non-COVID-19 Lung DiseasePreprint

Pizzi, A. D., Chiarelli, A. M., Chiacchiaretta, P., Valdesi, C., Croce, P., Mastrodicasa, D., Villani, M., Trebeschi, S., Serafini, F. M., Rosa, C., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-115659/v1

Integrity Attack Case Study: PCA Detector

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.006

Deep learning method for karate motion identification using inertial sensor data

Aihara, S., Ishibe, K., Sabu, R., & Iwata, H. (2021) · Transactions of the JSME (in Japanese) · doi:10.1299/transjsme.21-00214

MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER+/HER2- Early Breast Cancer PatientsPreprint

Pizzi, A. D., Mastrodicasa, D., Chiarelli, A. M., Chiacchiaretta, P., Luberti, R., Croce, P., Sguera, M., Torrione, C., Marinelli, C., Marchetti, C., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4034469

Introduction

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.001

References

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.015

Appendix D: Full Proofs for Near-Optimal Evasion

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.013

Appendix A: Background for Learning and Hyper-Geometry

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.010

Attacking a Hypersphere Learner

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.004

Privacy-Preserving Mechanisms for SVM Learning

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.007

Availability Attack Case Study: SpamBayes

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.005

Appendix B: Full Proofs for Hypersphere Attacks

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.011

Near-Optimal Evasion of Classifiers

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.008

Appendix C: Analysis of SpamBayes

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.012

Glossary

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.014

Bibliography

(2014) · Elsevier eBooks · doi:10.1016/b978-0-12-398537-8.00021-3

Peer Review #2 of "Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults (v0.1)"

(2017) · PeerJ · doi:10.7287/peerj.3508v0.1/reviews/2

Peer Review #1 of "Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults (v0.1)"

(2017) · PeerJ · doi:10.7287/peerj.3508v0.1/reviews/1

Peer Review #1 of "Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults (v0.3)"

(2017) · PeerJ · doi:10.7287/peerj.3508v0.3/reviews/1

Peer Review #1 of "Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults (v0.2)"

(2017) · PeerJ · doi:10.7287/peerj.3508v0.2/reviews/1

Index

Joseph, A. D., Nelson, B. A., Rubinstein, B. I. P., & Tygar, J. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9781107338548.016

Learning finite-state machines: statistical and algorithmic aspects

Balle, B. (2013) · doi:10.5821/dissertation-2117-95067

Algorithms and frameworks for preventing privacy leakage and overfitting in machine learning

Zakynthinou, L. (2023) · doi:10.17760/d20560798

Understanding The Stability Of Deep Learning from Weak Dependence PerspectivePreprint

Zou, K., Xu, J., Chen, Z., Dong, P., & Wang, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5071672

RBFL: Securing Federated Learning against Data Poisoning Using a Reputation-based Approach

Rehman, A., Mazu, I. I., Jaafar, F., Ameyed, D., & Abdessalem, H. B. (2025) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa66935.2025.11315401

Efficient Learning of Regularized Tyler's M -Estimator

Abou–Moustafa, K. T. (2026) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2026.3654886

Lower Bounds for Training and Leave-One-Out Estimates of the Generalization Error

Gavin, G., & Teytaud, O. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46084-5_95 · Cited by 2

The Study of Leave-One-Out Error-Based Classification Learning Algorithm for Generalization Performance

Zou, B., Xu, J., & Li, L. (2006) · Lecture notes in computer science · doi:10.1007/11881070_2

On Stability Analysis for Learning Algorithm under Condition of Leave-One-out

Ya-y, W. (2013) · Journal of Southwest China Normal University

Leave-One-Out Bounds for Kernel Methods

Zhang, T. (2003) · Neural Computation · doi:10.1162/089976603321780326 · Cited by 95

Stability revisited: new generalisation bounds for the Leave-one-OutPreprint

Célisse, A., & Guedj, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.06412 · Cited by 10