The DOI matches its Crossref record.
The Elements of Statistical Learning
Abstract
Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“To overcome the black-box nature of GBM, (Breiman 2001; Friedman and Popescu 2008; Hastie et al. 2009) introduced two important tools to interpret the GBM model: (i) variable importance measures and (ii) partial dependence plots.”
From Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting (2023) · cites this paper as Hastie et al. 2009
In the citing paper
“The training data is divided into a learning set (80%) and a validation set (20%) considering random folds of nearly the same size, mutually exclusive, and stratified (Hastie et al. 2009).”
From Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting (2023) · cites this paper as Hastie et al. 2009
In the citing paper
“See, for example,Jaggi (2013) andHastie, Tibshirani, and Friedman (2009), who discuss the equivalence of support vector machines with the lasso.”
From Empirical Asset Pricing via Machine Learning (2020) · cites this paper as Hastie, Tibshirani, and Friedman (2009)
In the citing paper
“Among these three types of impurity function, cross-entropy and the Gini index are more suitable than the misclassification rate when growing a tree.36”
From Predicting bitcoin returns using high-dimensional technical indicators (2019) · cites this paper as [36]
In the citing paper
“That is, for a given distribution, the optimal bandwidth depends on the number of examples (key-value pairs in associative memory).”
From Memory Mosaics at scale (2025) · cites this paper as Hastie et al., 2009
9 more claims about this paper
Create a free account to see every claim other papers make about this one.
Citations
Cited by
Items 701–800 of 1,448
Smith, P. F., Ganesh, S., & Liu, P. (2013) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.08.024 · Cited by 305
Wintle, B. A., Elith, J., & Potts, J. (2005) · Austral Ecology · doi:10.1111/j.1442-9993.2005.01514.x · Cited by 313
Ramsey, J., Glymour, M., Sánchez-Romero, R., & Glymour, C. (2016) · International Journal of Data Science and Analytics · doi:10.1007/s41060-016-0032-z · Cited by 334
Quantum Machine Learning: What Quantum Computing Means to Data Mining
Wittek, P. (2014) · Cited by 319
A systematic survey of computer-aided diagnosis in medicine: Past and present developments
Yanase, J., & Triantaphyllou, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112821 · Cited by 303
Of bits and wows: A Bayesian theory of surprise with applications to attention
Baldi, P., & Itti, L. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.12.007 · Cited by 313
Recent trends on hybrid modeling for Industry 4.0
Sansana, J., Joswiak, M. N., Castillo, I., Wang, Z., Rendall, R., Chiang, L. H., & Reis, M. S. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107365 · Cited by 323
Real-Time Convex Optimization in Signal Processing
Mattingley, J., & Boyd, S. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.936020 · Cited by 305
On label dependence and loss minimization in multi-label classification
Dembczyński, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2012) · Machine Learning · doi:10.1007/s10994-012-5285-8 · Cited by 300
Machine Learning for Network Automation: Overview, Architecture, and Applications [Invited Tutorial]
Rafique, D., & Velasco, L. (2018) · Journal of Optical Communications and Networking · doi:10.1364/jocn.10.00d126 · Cited by 298
Deep learning goes to school: toward a relational understanding of AI in education
Perrotta, C., & Selwyn, N. (2020) · Learning, Media and Technology · doi:10.1080/17439884.2020.1686017 · Cited by 299
Sun, L., Ji, S., & Ye, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.160 · Cited by 302
Recursive partitioning for missing data imputation in the presence of interaction effects
Doove, L. L., van Buuren, S., & Dusseldorp, E. (2013) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2013.10.025 · Cited by 312
Spoken Language Recognition: From Fundamentals to Practice
Li, H., Ma, B., & Lee, K. A. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2237151 · Cited by 304
Similarity-based Classification: Concepts and Algorithms
Chen, Y., Garcia, E. K., Gupta, M. R., Rahimi, A., & Cazzanti, L. (2009) · doi:10.5555/1577069.1577096 · Cited by 297
Recipe recommendation using ingredient networks
Teng, C.-Y., Lin, Y., & Adamic, L. A. (2012) · ACM Web Science Conference (WebSci) · doi:10.1145/2380718.2380757 · Cited by 303
Smoothing methods for nonsmooth, nonconvex minimization
Chen, X. (2012) · Mathematical Programming · doi:10.1007/s10107-012-0569-0 · Cited by 299
Materials Informatics: The Materials “Gene” and Big Data
Rajan, K. (2015) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070214-021132 · Cited by 310
Gayen, A., Pourghasemi, H. R., Saha, S., Keesstra, S. D., & Bai, S. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.02.436 · Cited by 303
Presence‐Only Data and the EM Algorithm
Ward, G., Hastie, T. J., Barry, S. C., Elith, J., & Leathwick, J. R. (2008) · Biometrics · doi:10.1111/j.1541-0420.2008.01116.x · Cited by 304
Self-citationMachine Learning for Health Services Researchers
Doupé, P., Faghmous, J. H., & Basu, S. (2019) · Value in Health · doi:10.1016/j.jval.2019.02.012 · Cited by 311
Data analytics in asset management: Cost-effective prediction of the pavement condition index
Piryonesi, S. M., & El-Diraby, T. E. (2020) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000512 · Cited by 295
Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice
Li, Y., Mathis, A., Grewe, B. F., Osterhout, J. A., Ahanonu, B., Schnitzer, M. J., Murthy, V. N., & Dulac, C. (2017) · Cell · doi:10.1016/j.cell.2017.10.015 · Cited by 291
Network dynamics of the brain and influence of the epileptic seizure onset zone
Burns, S. P., Santaniello, S., Yaffe, R. B., Jouny, C. C., Crone, N. E., Bergey, G. K., Anderson, W. S., & Sarma, S. V. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1401752111 · Cited by 296
The Pulse of News in Social Media: Forecasting Popularity
Bandari, R., Asur, S., & Huberman, B. A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14261 · Cited by 304
Predicting soil properties in the tropics
Minasny, B., & Hartemink, A. E. (2011) · Earth-Science Reviews · doi:10.1016/j.earscirev.2011.01.005 · Cited by 312
Locally Weighted Naive BayesPreprint
Frank, E., Hall, M., & Pfahringer, B. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1212.2487 · Cited by 300
Machine Learning for Sociology
Molina, M., & Garip, F. (2019) · Annual Review of Sociology · doi:10.1146/annurev-soc-073117-041106 · Cited by 295
Wolfers, T., Buitelaar, J. K., Beckmann, C. F., Franke, B., & Marquand, A. F. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.08.001 · Cited by 290
An insight into machine-learning algorithms to model human-caused wildfire occurrence
Rodrígues, M., & de la Riva, J. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.03.003 · Cited by 319
Outcome modelling strategies in epidemiology: traditional methods and basic alternatives
Greenland, S., Daniel, R., & Pearce, N. E. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw040 · Cited by 305
Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions
Andrade, J. R., & Bessa, R. J. (2017) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2017.2694340 · Cited by 292
Machine learning in geo- and environmental sciences: From small to large scale
Tahmasebi, P., Kamrava, S., Bai, T., & Sahimi, M. (2020) · Advances in Water Resources · doi:10.1016/j.advwatres.2020.103619 · Cited by 308
Svetnik, V., Liaw, A. I., Tong, C., & Wang, T. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25966-4_33 · Cited by 298
Hallquist, M. N., Wright, A. G. C., & Molenaar, P. C. M. (2021) · Multivariate Behavioral Research · doi:10.1080/00273171.2019.1640103 · Cited by 292
Tutorial in biostatistics: data‐driven subgroup identification and analysis in clinical trials
Lipkovich, I., Dmitrienko, A., & B., R. (2017) · Statistics in Medicine · doi:10.1002/sim.7064 · Cited by 291
Pourtaghi, Z. S., Pourghasemi, H. R., Aretano, R., & Semeraro, T. (2016) · Ecological Indicators · doi:10.1016/j.ecolind.2015.12.030 · Cited by 291
Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289
A Structure-Based Platform for Predicting Chemical Reactivity
Sandfort, F., Strieth‐Kalthoff, F., Kühnemund, M., Beecks, C., & Glorius, F. (2020) · Chem · doi:10.1016/j.chempr.2020.02.017 · Cited by 301
A data-driven predictive model of city-scale energy use in buildings
Kontokosta, C. E., & Tull, C. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.04.005 · Cited by 288
Beyond Distance Measurement: Constructing Neighborhood Similarity for Video Annotation
Wang, M., Hua, X.-S., Tang, J., & Hong, R. (2009) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2009.2012919 · Cited by 288
Clustering daily patterns of human activities in the city
Jiang, S., Ferreira, J., & González, M. C. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0264-z · Cited by 282
Autism risk associated with parental age and with increasing difference in age between the parents
Sandin, S., Schendel, D., Magnusson, P. K. E., Hultman, C. M., Surén, P., Susser, E., Grønborg, T. K., Gissler, M., Gunnes, N., Gross, R., et al. (2015) · Molecular Psychiatry · doi:10.1038/mp.2015.70 · Cited by 314
Hamstra, D. A., Chenevert, T. L., Moffat, B. A., Johnson, T. D., Meyer, C. R., Mukherji, S. K., Quint, D. J., Gebarski, S. S., Fan, X., Tsien, C. I., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0508347102 · Cited by 293
Sparse Signals in the Cross‐Section of Returns
Chinco, A., Clark-Joseph, A. D., & YE, M. (2018) · The Journal of Finance · doi:10.1111/jofi.12733 · Cited by 286
Non-stochastic Best Arm Identification and Hyperparameter OptimizationPreprint
Jamieson, K., & Talwalkar, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.07943 · Cited by 307
The 'K' in K-fold Cross Validation
Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., & Ridella, S. (2012) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 286
Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence
Alexandrov, T. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-011420-031537 · Cited by 310
Warren, D. L., Wright, A. N., Seifert, S. N., & Shaffer, H. B. (2014) · Diversity and Distributions · doi:10.1111/ddi.12160 · Cited by 284
The Subseasonal Experiment (SubX): A Multimodel Subseasonal Prediction Experiment
Pegion, K., Kirtman, B. P., Becker, E. J., Collins, D. C., LaJoie, E., Burgman, R. J., Bell, R., DelSole, T., Min, D., Zhu, Y., et al. (2019) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-18-0270.1 · Cited by 314
Oppel, S., Meirinho, A., Ramírez, I., Gardner, B., O’Connell, A. F., Miller, P. I., & Louzao, M. (2012) · Biological Conservation · doi:10.1016/j.biocon.2011.11.013 · Cited by 304
Least squares linear discriminant analysis
Ye, J. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273633 · Cited by 286
Müller, D., Leitão, P. J., & Sikor, T. O. (2013) · Agricultural Systems · doi:10.1016/j.agsy.2012.12.010 · Cited by 287
Robust estimation and classification for functional data via projection-based depth notions
Cuevas, A., Febrero–Bande, M., & Fraiman, R. (2007) · Computational Statistics · doi:10.1007/s00180-007-0053-0 · Cited by 287
Deep neural networks in psychiatry
Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 297
Kang, H., Zhang, A., Cai, T. T., & Small, D. S. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.994705 · Cited by 265
Dube, T. J., & Mutanga, O. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.11.001 · Cited by 281
Privacy-preserving matrix factorization
Nikolaenko, V., Ioannidis, S., Weinsberg, U., Jóye, M., Taft, N., & Boneh, D. (2013) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2508859.2516751 · Cited by 281
Examining the relationship between built environment and metro ridership at station-to-station level
Gan, Z., Yang, M., Feng, T., & Timmermans, H. J. P. (2020) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2020.102332 · Cited by 302
Ghannam, R. B., & Techtmann, S. M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.028 · Cited by 292
Proximal Methods for Hierarchical Sparse CodingPreprint
JenattonRodolphe, MairalJulien, ObozinskiGuillaume, & Bach, F. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1009.2139 · Cited by 281
Recommendations for Reporting Machine Learning Analyses in Clinical Research
Stevens, L. M., Mortazavi, B. J., Deo, R. C., Curtis, L., & Kao, D. P. (2020) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.120.006556 · Cited by 268
Modeling carbon allocation in trees: a search for principles
Franklin, O., Johansson, J., Dewar, R. C., Dieckmann, U., McMurtrie, R. E., Brännström, Å., & Dybzinski, R. (2012) · Tree Physiology · doi:10.1093/treephys/tpr138 · Cited by 294
Comparison of variable selection methods for clinical predictive modeling
Sanchez-Pinto, L. N., Venable, L. R., Fahrenbach, J. P., & Churpek, M. M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.05.006 · Cited by 298
van de Sande, D., van Genderen, M. E., Huiskens, J., Gommers, D., & van Bommel, J. (2021) · Intensive Care Medicine · doi:10.1007/s00134-021-06446-7 · Cited by 279
Speeding Up Logistic Model Tree Induction
Sumner, M., Frank, E., & Hall, M. (2005) · Lecture Notes in Computer Science · doi:10.1007/11564126_72 · Cited by 274
Social presence in online discussions as a process predictor of academic performance
Joksimović, S., Gašević, D., Kovanović, V., Riecke, B. E., & Hatala, M. (2015) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12107 · Cited by 278
Predictive soil parent material mapping at a regional-scale: A Random Forest approach
Heung, B., Bulmer, C., & Schmidt, M. (2013) · Geoderma · doi:10.1016/j.geoderma.2013.09.016 · Cited by 284
A comparison of resampling methods for remote sensing classification and accuracy assessment
Lyons, M., Keith, D. A., Phinn, S., Mason, T. J., & Elith, J. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.02.026 · Cited by 278
Extended Target Tracking using a Gaussian-Mixture PHD Filter
Granström, K., Lundquist, C., & Orguner, U. (2012) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2012.6324703 · Cited by 282
Ye, T., Zhao, N., Yang, X., Ouyang, Z., Liu, X., Chen, Q., Hu, K., Yue, W., Qi, J., Li, Z., et al. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.12.276 · Cited by 276
Data-derived soft-sensors for biological wastewater treatment plants: An overview
Haimi, H., Mulas, M., Corona, F., & Vahala, R. (2013) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2013.05.009 · Cited by 285
Penalized model-based clustering with application to variable selection
Pan, W., Shen, X., & Yu, B. (2007) · Cited by 266
Statistical Foundations for Model-Based Adjustments
Greenland, S., & Pearce, N. E. (2015) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031914-122559 · Cited by 286
Identifying Encrypted Malware Traffic with Contextual Flow Data
Anderson, B., & McGrew, D. A. (2016) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2996758.2996768 · Cited by 288
Stafford, I. S., Kellermann, M., Mossotto, E., Beattie, R. M., MacArthur, B. D., & Ennis, S. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0229-3 · Cited by 275
Multilabel Classification with Principal Label Space Transformation
Tai, F., & Lin, H.-T. (2012) · Neural Computation · doi:10.1162/neco_a_00320 · Cited by 278
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
Concas, F., Mineraud, J., Lagerspetz, E., Varjonen, S., Liu, X., Puolamäki, K., Nurmi, P., & Tarkoma, S. (2021) · ACM Transactions on Sensor Networks · doi:10.1145/3446005 · Cited by 334
Mapping epistemic uncertainties and vague concepts in predictions of species distribution
Elith, J., Burgman, M. A., & Regan, H. M. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00202-8 · Cited by 278
Soil Property and Class Maps of the Conterminous United States at 100‐Meter Spatial Resolution
Ramcharan, A., Tomislav Hengl, A., Nauman, T., Brungard, C., Waltman, S. W., Wills, S., & Thompson, J. A. (2018) · Soil Science Society of America Journal · doi:10.2136/sssaj2017.04.0122 · Cited by 282
Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review
Kamišalić, A., Fister, I., Turkanović, M., & Karakatič, S. (2018) · Sensors · doi:10.3390/s18061714 · Cited by 272
High-Dimensional Statistics with a View Toward Applications in Biology
Bühlmann, P., Kalisch, M., & Meier, L. (2014) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-022513-115545 · Cited by 289
Identification of molecular subtypes of glioblastoma by gene expression profiling
Mischel, P. S., Mehrian‐Shai, R., Shi, T., Horvath, S., Lu, K. V., Choe, G., Seligson, D. B., Kremen, T. J., Palotie, A. V., Liau, L. M., et al. (2003) · Oncogene · doi:10.1038/sj.onc.1206344 · Cited by 278
Slade, G. D., Bair, E., Greenspan, J. D., Dubner, R. A., Fillingim, R. B., Diatchenko, L. B., Maixner, W. D., Knott, C. E., & Ohrbach, R. K. (2013) · Journal of Pain · doi:10.1016/j.jpain.2013.07.014 · Cited by 271
A brief survey of machine learning methods and their sensor and IoT applications
Shanthamallu, U. S., Spanias, A., Tepedelenlioğlu, C., & Stanley, M. (2017) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa.2017.8316459 · Cited by 281
Boer, M. P., Wright, D., Feng, L., Podlich, D. W., Luo, L., Cooper, M., & van Eeuwijk, F. A. (2007) · Genetics · doi:10.1534/genetics.107.071068 · Cited by 264
Mining opinions from instructor evaluation reviews: a deep learning approach
Onan, A. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22179 · Cited by 270
Davatzikos, C. A., Shen, D. G., Gur, R. C., Wu, X., Liu, D., Yong Fan, A., Hughett, P., Turetsky, B. I., & Gur, R. E. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.11.1218 · Cited by 269
Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
Liu, Y., Hayes, D. N., Nobel, A. B., & Marron, J. S. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000454 · Cited by 273
Ghadimi, S., & Lan, G. (2012) · SIAM Journal on Optimization · doi:10.1137/110848864 · Cited by 279
Kang, Y., Weber, K. D., Qiu, Y. L., Kiley, P. J., & Blattner, F. R. (2005) · Journal of Bacteriology · doi:10.1128/jb.187.3.1135-1160.2005 · Cited by 279
Towards felicitous decision making: An overview on challenges and trends of Big Data
Wang, H., Xu, Z., Fujita, H., & Liu, S. (2016) · Information Sciences · doi:10.1016/j.ins.2016.07.007 · Cited by 286
Knoll, L., Breuer, L., & Bach, M. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.03.045 · Cited by 269
Root zone soil moisture estimation with Random Forest
Carranza, C., Nolet, C., Pezij, M., & van der Ploeg, M. J. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125840 · Cited by 283
Wirbel, J., Zych, K., Essex, M., Karcher, N., Kartal, E., Salazar, G., Bork, P., Sunagawa, S., & Zeller, G. (2021) · Genome Biology · doi:10.1186/s13059-021-02306-1 · Cited by 269
Mapping land-cover modifications over large areas: A comparison of machine learning algorithms
Rogan, J. S., Franklin, J., Stow, D. A., Miller, J. A., Woodcock, C. E., & Roberts, D. A. (2008) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.10.004 · Cited by 281
Bus Arrival Time Prediction Using Support Vector Machines
Yu, B., Zhongzhen, Y., & Baozhen, Y. (2006) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450600981009 · Cited by 273
Prediction of Dengue Incidence Using Search Query Surveillance
Althouse, B. M., Ng, Y. Y., & Cummings, D. A. T. (2011) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0001258 · Cited by 273
Measuring socioeconomic status in multicountry studies: results from the eight-country MAL-ED study
Psaki, S. R., Seidman, J. C., Miller, M. D., Gottlieb, M., Bhutta, Z. A., Ahmed, T., Ahmed, A. M. S., Bessong, P. O., John, S. M., Kang, G., et al. (2014) · Population Health Metrics · doi:10.1186/1478-7954-12-8 · Cited by 277
Gene selection using support vector machines with non-convex penalty
Zhang, H. H., Ahn, J., Lin, X. S., & Park, C. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti736 · Cited by 269
Metadata
- Authors
- Hastie, T., Tibshirani, R., & Friedman, J.
- Affiliations
- Stanford University (US)
- Venue
- Springer Series in Statistics
- Publisher
- Springer New York
- Published
- 2009
- ISSN
- 0172-7397 (ISSN-L)
- 2197-568X
- 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.
