Cited by 25,970Statistical Methods and ApplicationsStatistical Methods and InferenceMachine Learning and Algorithms

The DOI matches its Crossref record.

The Elements of Statistical Learning

Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome·2009·Springer Series in Statistics

Open this paper in CoChat — free account

Abstract

Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting

Integrity signals

Registry

Crossref · 10.1007/978-0-387-84858-7

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

9 more claims about this paper

Create a free account to see every claim other papers make about this one.

See all claims — free account

Items 1,201–1,300 of 1,448

A new class of distributed optimization algorithms: Application to regression of distributed data
Sundhar Ram, S., Nedić, A., & Veeravalli, V. V. (2012) · Optimization Methods and Software · doi:10.1080/10556788.2010.511669 · Cited by 123
Finding missed cases of familial hypercholesterolemia in health systems using machine learning
Banda, J. M., Sarraju, A., Abbasi, F., Parizo, J., Pariani, M., Ison, H., Briskin, E., Wand, H., Dubois, S., Jung, K., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0101-5 · Cited by 123
Statistical Model for Forecasting Monthly Large Wildfire Events in Western United States
Preisler, H. K., & Westerling, A. L. (2007) · Journal of Applied Meteorology and Climatology · doi:10.1175/jam2513.1 · Cited by 123
Genome-Wide Association Study of Suicide Death and Polygenic Prediction of Clinical Antecedents
Docherty, A. R., Shabalin, A. A., DiBlasi, E., Monson, E., Mullins, N., Adkins, D. E., Bacanu, S.-A., Bakian, A. V., Crowell, S., Chen, D., et al. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.19101025 · Cited by 124
EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124
Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques
Acebes, F., Pereda, M., Poza, D., Pajares, J., & Galán, J. M. (2015) · International Journal of Project Management · doi:10.1016/j.ijproman.2015.06.012 · Cited by 120
Enabling stroke rehabilitation in home and community settings: a wearable sensor-based approach for upper-limb motor training
Lee, S. I., Adans-Dester, C. P., Grimaldi, M., Dowling, A. V., Horak, P. C., Black-Schaffer, R. M., Bonato, P., & Gwin, J. T. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2829208 · Cited by 119
Cortical thickness predicts the first onset of major depression in adolescence
Foland‐Ross, L. C., Sacchet, M. D., Prasad, G., Gilbert, B., Thompson, P. M., & Gotlib, I. H. (2015) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2015.07.007 · Cited by 109
AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature
Birgmeier, J., Haeussler, M., Deisseroth, C. A., Steinberg, E. H., Jagadeesh, K. A., Ratner, A. J., Guturu, H., Wenger, A. M., Diekhans, M. E., Stenson, P. D., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aau9113 · Cited by 118
Improved marketing decision making in a customer churn prediction context using generalized additive models
Coussement, K., Benoit, D. F., & Van den Poel, D. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.07.029 · Cited by 117
Predicting the progression of mild cognitive impairment using machine learning: a systematic, quantitative and critical review
Ansart, M., Epelbaum, S., Bassignana, G., Bône, A., Bottani, S., Cattai, T., Couronné, R., Faouzi, J., Koval, I., Louis, M., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101848 · Cited by 116
Predicting Developmental Status from 12 to 24 Months in Infants at Risk for Autism Spectrum Disorder: A Preliminary Report
Macari, S. L., Campbell, D., Gengoux, G. W., Saulnier, C. A., Klin, A. J., & Chawarska, K. (2012) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-012-1521-0 · Cited by 116
Novel proximal sensing for monitoring soil organic C stocks and condition
Viscarra Rossel, R. A., Lobsey, C. R., Sharman, C., Flick, P., & McLachlan, G. (2017) · Environmental Science & Technology · doi:10.1021/acs.est.7b00889 · Cited by 116
Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism
Ghiassian, S., Greiner, R., Jin, P., & Brown, M. R. G. (2016) · PLOS ONE · doi:10.1371/journal.pone.0166934 · Cited by 116
Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism
Plitt, M., Barnes, K. A., Wallace, G. L., Kenworthy, L., & Martin, A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510098112 · Cited by 114
The ideological basis of antiscientific attitudes: Effects of authoritarianism, conservatism, religiosity, social dominance, and system justification
Azevedo, F., & Jost, J. T. (2021) · Group Processes & Intergroup Relations · doi:10.1177/1368430221990104 · Cited by 113
Functional subpopulations of V3 interneurons in the mature mouse spinal cord
Borowska, J., Jones, C. T., Zhang, H., Blacklaws, J., Goulding, M., & Zhang, Y. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2005-13.2013 · Cited by 111
Large-scale identification of patients with cerebral aneurysms using natural language processing
Castro, V. M., Dligach, D., Finan, S., Yu, S., Can, A., Abd-El-Barr, M., Gainer, V., Shadick, N. A., Murphy, S., Cai, T., et al. (2017) · Neurology · doi:10.1212/wnl.0000000000003490 · Cited by 98
Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review
Sanchez-Morillo, D., Fernandez-Granero, M. A., & Leon-Jimenez, A. (2016) · Chronic Respiratory Disease · doi:10.1177/1479972316642365 · Cited by 110
Engineering the Oleaginous Yeast Yarrowia Lipolytica to Produce the Aroma Compound β-Ionone
Czajka, J. J., Nathenson, J. A., Benites, V. T., Baidoo, E. E. K., Cheng, Q., Wang, Y., & Tang, Y. J. (2018) · Microbial Cell Factories · doi:10.1186/s12934-018-0984-x · Cited by 109
Integration of multi-omics data for prediction of phenotypic traits using random forest
Acharjee, A., Kloosterman, B., Visser, R. G. F., & Maliepaard, C. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1043-4 · Cited by 109
Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood
Schultebraucks, K., Yadav, V., Shalev, A. Y., Bonanno, G. A., & Galatzer-Levy, I. R. (2022) · Psychological Medicine · doi:10.1017/s0033291720002718 · Cited by 108
Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads
Li, W., & Jiang, T. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts559 · Cited by 97
Machine learning consensus scoring improves performance across targets in structure-based virtual screening.
Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00153 · Cited by 107
A novel, data-driven heuristic framework for vessel weather routing
Gkerekos, C., & Lazakis, I. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2019.106887 · Cited by 107
Automated Decision Tree Classification of Corneal Shape
Twa, M. D., Parthasarathy, S., Roberts, C., Mahmoud, A. M., Raasch, T. W., & Bullimore, M. A. (2005) · Optometry and Vision Science · doi:10.1097/01.opx.0000192350.01045.6f · Cited by 108
Beyond generalization: a theory of robustness in machine learning
Freiesleben, T., & Grote, T. (2023) · Synthese · doi:10.1007/s11229-023-04334-9 · Cited by 106
A genome-wide association study identifies multiple loci for variation in human ear morphology
Adhikari, K., Reales, G., Smith, A. J. P., Konka, E., Palmen, J., Quinto-Sanchez, M., Acuña-Alonzo, V., Jaramillo, C., Arias, W., Fuentes, M., et al. (2015) · Nature Communications · doi:10.1038/ncomms8500 · Cited by 95
Trends in Classroom Observation Scores
Casabianca, J. M., Lockwood, J. R., & McCaffrey, D. F. (2015) · Educational and Psychological Measurement · doi:10.1177/0013164414539163 · Cited by 105
Deep learning-enabled multi-organ segmentation in whole-body mouse scans
Schoppe, O., Pan, C., Coronel, J., Mai, H., Rong, Z., Todorov, M. I., Müskes, A., Navarro, F., Li, H., Ertürk, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19449-7 · Cited by 104
Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime
Sendek, A. D., Ransom, B., Cubuk, E. D., Pellouchoud, L. A., Nanda, J., & Reed, E. J. (2022) · Advanced Energy Materials · doi:10.1002/aenm.202200553 · Cited by 100
Enhancing Explainability of Neural Networks Through Architecture Constraints
Yang, Z., Zhang, A., & Sudjianto, A. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3007259 · Cited by 106
Advancing predictive modeling in archaeology: an evaluation of regression and machine learning methods on the grand staircase-escalante national monument
Yaworsky, P. M., Vernon, K. B., Spangler, J. D., Brewer, S. C., & Codding, B. F. (2020) · PLOS One · doi:10.1371/journal.pone.0239424 · Cited by 104
Organizing memories for generalization in complementary learning systems
Sun, W., Advani, M., Spruston, N., Saxe, A., & Fitzgerald, J. E. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01382-9 · Cited by 103
Sensing Behavioral Change over Time: Using Within-Person Variability Features from Mobile Sensing to Predict Personality Traits
Wang, W., Harari, G. M., Wang, R., Müller, S. R., Mirjafari, S., Masaba, K., & Campbell, A. T. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3264951 · Cited by 100
The Computerized Adaptive Diagnostic Test for Major Depressive Disorder (CAD-MDD): A Screening Tool for Depression
Gibbons, R. D., Hooker, G., Finkelman, M. D., Weiss, D. J., Pilkonis, P. A., Frank, E., Moore, T., & Kupfer, D. J. (2013) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.12m08338 · Cited by 101
Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics.
Adhikari, L., Ozrazgat-Baslanti, T., Ruppert, M., Madushani, R. W. M. A., Paliwal, S., Hashemighouchani, H., Zheng, F., Tao, M., Lopes, J. M., Li, X., et al. (2019) · PLOS ONE · doi:10.1371/journal.pone.0214904 · Cited by 98
Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics
Rice, J. S., Emanuel, R. E., Vose, J. M., & Nelson, S. A. C. (2015) · Water Resources Research · doi:10.1002/2014wr016367 · Cited by 95
Interactions between risk factors in the prediction of onset of eating disorders: Exploratory hypothesis generating analyses
Stice, E., & Desjardins, C. D. (2018) · Behaviour Research and Therapy · doi:10.1016/j.brat.2018.03.005 · Cited by 98
DALSA: domain adaptation for supervised learning from sparsely annotated MR images
Goetz, M., Weber, C., Binczyk, F., Polanska, J., Tarnawski, R., Bobek-Billewicz, B., Koethe, U., Kleesiek, J., Stieltjes, B., & Maier-Hein, K. H. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2463078 · Cited by 98
The role of low-grade inflammation and metabolic flexibility in aging and nutritional modulation thereof: a systems biology approach
Calçada, D., Vianello, D., Giampieri, E., Sala, C., Castellani, G., de Graaf, A., Kremer, B., van Ommen, B., Feskens, E., Santoro, A., et al. (2014) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2014.01.004 · Cited by 97
Heading for new shores! Overcoming pitfalls in BCI design
Chavarriaga, R., Fried-Oken, M., Kleih, S., Lotte, F., & Scherer, R. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2016.1263916 · Cited by 97
Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory
Kruppa, J., Liu, Y., Biau, G., Kohler, M., König, I. R., Malley, J. D., & Ziegler, A. (2014) · Biometrical Journal · doi:10.1002/bimj.201300068 · Cited by 97
Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review
Dhombres, F., Bonnard, J., Bailly, K., Maurice, P., Papageorghiou, A. T., & Jouannic, J.-M. (2022) · Journal of Medical Internet Research · doi:10.2196/35465 · Cited by 93
Tree-based ensemble methods for sensitivity analysis of environmental models: A performance comparison with Sobol and Morris techniques
Jaxa-Rozen, M., & Kwakkel, J. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2018.06.011 · Cited by 91
Policing the homeless: An evaluation of efforts to reduce homeless‐related crime
Berk, R., & MacDonald, J. (2010) · Criminology & Public Policy · doi:10.1111/j.1745-9133.2010.00673.x · Cited by 93
Contrastive Learning Based Modality-Invariant Feature Acquisition for Robust Multimodal Emotion Recognition With Missing Modalities
Liu, R., Zuo, H., Lian, Z., Schuller, B. W., & Li, H. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3378570 · Cited by 96
Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma
Chortis, V., Bancos, I., Nijman, T., Gilligan, L. C., Taylor, A. E., Ronchi, C. L., O’Reilly, M. W., Schreiner, J., Asia, M., Riester, A., et al. (2020) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgz141 · Cited by 93
Decoding developmental differences and individual variability in response inhibition through predictive analyses across individuals
Cohen, J. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00047 · Cited by 89
Detection of Cyber-attacks to indoor real time localization systems for autonomous robots
Guerrero-Higueras, Á. M., DeCastro-García, N., & Matellán, V. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2017.10.006 · Cited by 91
Explainable AI and Multi-Modal Causability in Medicine
Holzinger, A. (2021) · i-com · doi:10.1515/icom-2020-0024 · Cited by 87
Data science as machinic neoplatonism
McQuillan, D. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0273-3 · Cited by 90
Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry
Ioannidis, K., Chamberlain, S. R., Treder, M. S., Kiraly, F., Leppink, E. W., Redden, S. A., Stein, D. J., Lochner, C., & Grant, J. E. (2016) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2016.08.010 · Cited by 90
Combined mRNA microarray and proteomic analysis of eutopic endometrium of women with and without endometriosis
Fassbender, A., Verbeeck, N., Börnigen, D., Kyama, C. M., Bokor, A., Vodolazkaia, A., Peeraer, K., Tomassetti, C., Meuleman, C., Gevaert, O., et al. (2012) · Human Reproduction · doi:10.1093/humrep/des127 · Cited by 87
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning
Schelter, S., Grafberger, S., & Dunning, T. (2021) · Proceedings of the 2021 International Conference on Management of Data · doi:10.1145/3448016.3457239 · Cited by 89
A SVR-GWO technique to minimize flyrock distance resulting from blasting
Armaghani, D. J., Koopialipoor, M., Bahri, M., Hasanipanah, M., & Tahir, M. M. (2020) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-020-01834-7 · Cited by 85
Quantitative ultrasound texture analysis of fetal lungs to predict neonatal respiratory morbidity
Bonet‐Carne, E., Palacio, M., Cobo, T., Perez‐Moreno, A., Lopez, M., Piraquive, J. P., Ramirez, J. C., Botet, F., Marques, F., & Gratacos, E. (2015) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.13441 · Cited by 85
Predictors of attrition in a longitudinal population-based study of aging
Jacobsen, E., Ran, X., Liu, A., Chang, C.-C. H., & Ganguli, M. (2021) · International Psychogeriatrics · doi:10.1017/s1041610220000447 · Cited by 85
A machine-learning heuristic to improve gene score prediction of polygenic traits
Paré, G., Mao, S., & Deng, W. Q. (2017) · Scientific Reports · doi:10.1038/s41598-017-13056-1 · Cited by 85
Predicting treatment response to antidepressant medication using early changes in emotional processing
Browning, M., Kingslake, J., Dourish, C. T., Goodwin, G. M., Harmer, C. J., & Dawson, G. R. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2018.11.1102 · Cited by 83
Stereo-Camera-Based Urban Environment Perception Using Occupancy Grid and Object Tracking
Nguyen, T.-N., Michaelis, B., Al-Hamadi, A., Tornow, M., & Meinecke, M.-M. (2012) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2165705 · Cited by 80
Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids
Acosta-Pech, R., Crossa, J., de los Campos, G., Teyssèdre, S., Claustres, B., Pérez-Elizalde, S., & Pérez-Rodríguez, P. (2017) · Theoretical and Applied Genetics · doi:10.1007/s00122-017-2898-0 · Cited by 74
Predicting imminent suicidal thoughts and nonfatal attempts: The role of complexity
Ribeiro, J. D., Huang, X., Fox, K. R., Walsh, C. G., & Linthicum, K. P. (2019) · Clinical Psychological Science · doi:10.1177/2167702619838464 · Cited by 75
A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging
Kaissis, G., Ziegelmayer, S., Lohöfer, F., Algül, H., Eiber, M., Weichert, W., Schmid, R., Friess, H., Rummeny, E., Ankerst, D., et al. (2019) · European Radiology Experimental · doi:10.1186/s41747-019-0119-0 · Cited by 79
A Modeling Framework for Deriving the Structural and Functional Architecture of a Short-Term Memory Microcircuit
Fisher, D., Olasagasti, I., Tank, D. W., Aksay, E. R. F., & Goldman, M. S. (2013) · Neuron · doi:10.1016/j.neuron.2013.06.041 · Cited by 75
A general path-based representation for predicting program properties
Alon, U., Zilberstein, M., Levy, O., & Yahav, E. (2018) · Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/3192366.3192412 · Cited by 79
Inferring input nonlinearities in neural encoding models
Ahrens, M. B., Paninski, L., & Sahani, M. (2008) · Network Computation in Neural Systems · doi:10.1080/09548980701813936 · Cited by 72
Effects of compassion meditation on a psychological model of charitable donation.
Ashar, Y. K., Andrews-Hanna, J. R., Yarkoni, T., Sills, J., Halifax, J., Dimidjian, S., & Wager, T. D. (2016) · Emotion · doi:10.1037/emo0000119 · Cited by 78
Identifying an optimal cutpoint for the diagnosis of hypertriglyceridemia in the nonfasting state
White, K. T., Moorthy, M. V., Akinkuolie, A. O., Demler, O., Ridker, P. M., Cook, N. R., & Mora, S. (2015) · Clinical Chemistry · doi:10.1373/clinchem.2015.241752 · Cited by 74
Survival Prediction Models since Liver Transplantation - Comparisons between Cox Models and Machine Learning Techniques
Kantidakis, G., Putter, H., Lancia, C., Boer, J. D., Braat, A. E., & Fiocco, M. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01153-1 · Cited by 75
Targeted scoring functions for virtual screening.
Seifert, M. H. J. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.03.013 · Cited by 76
Characterizing user engagement with health app data: a data mining approach
Serrano, K. J., Coa, K. I., Yu, M., Wolff-Hughes, D. L., & Atienza, A. A. (2017) · Translational Behavioral Medicine · doi:10.1007/s13142-017-0508-y · Cited by 67
Machine Learning-Based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-Of-Concept Study
Grosu, S., Wesp, P., Graser, A., Maurus, S., Schulz, C., Knösel, T., Cyran, C. C., Ricke, J., Ingrisch, M., & Kazmierczak, P. M. (2021) · Radiology · doi:10.1148/radiol.2021202363 · Cited by 75
Methylome-wide association findings for major depressive disorder overlap in blood and brain and replicate in independent brain samples
Aberg, K. A., Dean, B., Shabalin, A. A., Chan, R. F., Han, L. K. M., Zhao, M., van Grootheest, G., Xie, L. Y., Milaneschi, Y., Clark, S. L., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-018-0247-6 · Cited by 73
A serious game for children with Attention Deficit Hyperactivity Disorder: Who benefits the most?
Bul, K. C. M., Doove, L. L., Franken, I. H. A., Oord, S. V. D., Kato, P. M., & Maras, A. (2018) · PLOS ONE · doi:10.1371/journal.pone.0193681 · Cited by 71
Sparse Group Lasso: Consistency and Climate Applications
Chatterjee, S., Steinhaeuser, K., Banerjee, A., Chatterjee, S., & Ganguly, A. (2012) · Proceedings of the 2012 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972825.5 · Cited by 72
AI system engineering key challenges and lessons learned
Fischer, L., Ehrlinger, L., Geist, V., Ramler, R., Sobiezky, F., Zellinger, W., Brunner, D., Kumar, M., & Moser, B. (2020) · Machine Learning and Knowledge Extraction · doi:10.3390/make3010004 · Cited by 71
Medial prefrontal aberrations in major depressive disorder revealed by cytoarchitectonically informed voxel-based morphometry
Bludau, S., Bzdok, D., Gruber, O., Kohn, N., Riedl, V., Sorg, C., Palomero-Gallagher, N., Müller, V. I., Hoffstaedter, F., Amunts, K., et al. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15030349 · Cited by 67
Improving Cancer Outreach Effectiveness Through Targeting and Economic Assessments: Insights from a Randomized Field Experiment
Chen, Y., Lee, J.-Y., Sridhar, S. (., Mittal, V., McCallister, K., & Singal, A. G. (2020) · Journal of Marketing · doi:10.1177/0022242920913025 · Cited by 70
Evidence for the effect of homes on wildfire suppression costs
Gude, P. H., Jones, K., Rasker, R., & Greenwood, M. C. (2013) · International Journal of Wildland Fire · doi:10.1071/wf11095 · Cited by 69
Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes
Ghosh, A., & Joshi, P. K. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.07.003 · Cited by 69
Improving random forests by neighborhood projection for effective text classification
Salles, T., Gonçalves, M., Rodrigues, V., & Rocha, L. (2018) · Information Systems · doi:10.1016/j.is.2018.05.006 · Cited by 66
Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
Turner, O. C., Aeffner, F., Bangari, D. S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L. E., Rudmann, D. G., Bawa, B., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623319881401 · Cited by 64
Oxytocin receptor genotype modulates ventral striatal activity to social cues and response to stressful life events
Loth, E., Poline, J.-B., Thyreau, B., Jia, T., Tao, C., Lourdusamy, A., Stacey, D., Cattrell, A., Desrivières, S., Ruggeri, B., et al. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.07.043 · Cited by 65
Development and Validation of a Machine Learning-Based Decision Support Tool for Residency Applicant Screening and Review
Burk-Rafel, J., Reinstein, I., Feng, J., Kim, M. B., Miller, L. H., Cocks, P. M., Marin, M., & Aphinyanaphongs, Y. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004317 · Cited by 65
Cost Overrun Risk Assessment and Prediction in Construction Projects: A Bayesian Network Classifier Approach
Ashtari, M. A., Ansari, R., Hassannayebi, E., & Jeong, J. (2022) · Buildings · doi:10.3390/buildings12101660 · Cited by 65
Discovering cognitive strategies with tiny recurrent neural networks
Ji-An, L., Benna, M. K., & Mattar, M. G. (2025) · Nature · doi:10.1038/s41586-025-09142-4 · Cited by 64
Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning
Koçak, B., Cuocolo, R., Santos, D. P. D., Stanzione, A., & Ugga, L. (2023) · Balkan Medical Journal · doi:10.4274/balkanmedj.galenos.2022.2022-11-51 · Cited by 64
Machine Learning for Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety after Myocardial Infarction: Insights from the U-CARE Heart Trial (Preprint)
Wallert, J., Gustafson, E., Held, C., Madison, G., Norlund, F., von Essen, L., & Olsson, E. M. G. (2018) · doi:10.2196/preprints.10754 · Cited by 63
A cognitive analytics management framework for the transformation of electronic government services from users’ perspective to create sustainable shared values
Osman, I. H., Anouze, A. L., Irani, Z., Lee, H., Medeni, T. D., & Weerakkody, V. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.02.018 · Cited by 63
Machine learning and artificial intelligence: applications in healthcare epidemiology
Hamilton, A. J., Strauss, A. T., Martinez, D. A., Hinson, J. S., Levin, S., Lin, G., & Klein, E. Y. (2021) · Antimicrobial Stewardship & Healthcare Epidemiology · doi:10.1017/ash.2021.192 · Cited by 58
Improving Graph Attention Networks with Large Margin-based Constraints
Wang, G., Ying, R., Huang, J., & Leskovec, J. (2019) · arXiv · doi:10.48550/arxiv.1910.11945 · Cited by 61
Data-Driven Metro Train Crowding Prediction Based on Real-Time Load Data
Jenelius, E. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2914729 · Cited by 60
Using Data Mining for Wine Quality Assessment
Cortez, P., Teixeira, J., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-04747-3_8 · Cited by 60
Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis
Creagh, A. P., Hamy, V., Yuan, H., Mertes, G., Tomlinson, R., Chen, W.-H., Williams, R., Llop, C., Yee, C., Duh, M. S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01013-y · Cited by 64
Determining the Veracity of Rumours on Twitter
Giasemidis, G., Singleton, C., Agrafiotis, I., Nurse, J. R. C., Pilgrim, A., Willis, C., & Greetham, D. V. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-47880-7_12 · Cited by 60
Data-Driven Studies of Li-Ion-Battery Materials
Kauwe, S. K., Rhone, T. D., & Sparks, T. D. (2019) · Crystals · doi:10.3390/cryst9010054 · Cited by 57
Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science
Radford, J., & Joseph, K. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00018 · Cited by 58
Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
de Prado, M., Rusci, M., Capotondi, A., Donze, R., Benini, L., & Pazos, N. (2021) · Sensors · doi:10.3390/s21041339 · Cited by 57
Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design
Janet, J. P., Gani, T. Z. H., Steeves, A. H., Ioannidis, E. I., & Kulik, H. J. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b00808 · Cited by 56

Authors: Hastie, T., Tibshirani, R., & Friedman, J.
Affiliations: Stanford University (US)
ORCID: Hastie, Trevor
Tibshirani, Robert
Friedman, Jerome
Venue: Springer Series in Statistics
Publisher: Springer New York
Published: 2009
ISSN: 0172-7397 (ISSN-L)
2197-568X
DOI: 10.1007/978-0-387-84858-7
OpenAlex: W2787894218
Cited by: 25,970
References: 0
Citations by year: 2026: 3306 · 2025: 3252 · 2024: 2901 · 2023: 2360 · 2022: 2154 · 2021: 2134 · 2020: 1664 · 2019: 1391 · 2018: 1170 · 2017: 1082 · 2016: 1024 · 2015: 1009 · 2014: 725 · 2013: 623 · 2012: 513
FWCI: 61.16
Type: book
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Springer series in statistics (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords: statistical learning, machine learning, statistical inference, data analysis, applied statistics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.

The Elements of Statistical Learning

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks

Data sources