The Elements of Statistical Learning

A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression

Li, T., Zhang, C., & Ogihara, M. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth267 · Cited by 653

Reconstructing Speech from Human Auditory Cortex

Pasley, B. N., David, S. V., Mesgarani, N., Flinker, A., Shamma, S. A., Crone, N. E., Knight, R. T., & Chang, E. F. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001251 · Cited by 604

Transition-Path Theory and Path-Finding Algorithms for the Study of Rare Events

Weinan, E., & Vanden‐Eijnden, E. (2008) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.040808.090412 · Cited by 661

A European Respiratory Society technical standard: exhaled biomarkers in lung disease

Horváth, I., Barnes, P. J., Loukides, S., Sterk, P. J., Högman, M., Olin, A., Amann, A., Antus, B., Baraldi, E., Bikov, A., et al. (2017) · European Respiratory Journal · doi:10.1183/13993003.00965-2016 · Cited by 673

Sequence signatures and mRNA concentration can explain two‐thirds of protein abundance variation in a human cell line

Vogel, C., de Sousa Abreu, R., Ko, D., Le, S., Shapiro, B. A., Burns, S. C., Sandhu, D., Boutz, D. R., Marcotte, E. M., & Penalva, L. O. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.59 · Cited by 608

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Razavi, S., Jakeman, A. J., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Piano, S. L., Iwanaga, T., Becker, W., et al. (2020) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2020.104954 · Cited by 713

Continued warming could transform Greater Yellowstone fire regimes by mid-21st century

Westerling, A. L., Turner, M. G., Smithwick, E. A. H., Romme, W. H., & Ryan, M. G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1110199108 · Cited by 650

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

Olson, R. S., Bartley, N., Urbanowicz, R. J., & Moore, J. H. (2016) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2908812.2908918 · Cited by 679

Locating Facial Features with an Extended Active Shape Model

Milborrow, S., & Nicolls, F. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88693-8_37 · Cited by 627

Modelling distribution and abundance with presence‐only data

Pearce, J., & Boyce, M. S. (2005) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2005.01112.x · Cited by 647

Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

Cantor, R. M., Lange, K. L., & Sinsheimer, J. S. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.11.017 · Cited by 643

Comparing Landsat water index methods for automated water classification in eastern Australia

Fisher, A. G., Flood, N., & Danaher, T. J. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.12.055 · Cited by 617

Valid post-selection inference

Berk, R. A., Brown, L., Buja, A., Zhang, K., & Zhao, L. H. (2013) · The Annals of Statistics · doi:10.1214/12-aos1077 · Cited by 627

Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms

Xue, B., Zhang, M., & Browne, W. N. (2013) · Applied Soft Computing · doi:10.1016/j.asoc.2013.09.018 · Cited by 614

Data Streams: Models and Algorithms

Aggarwal, C. C. (2014) · Springer eBooks · doi:10.1007/978-0-387-47534-9 · Cited by 611

GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation

Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., & Rui, Y. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623638 · Cited by 608

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility

Moore, J. H., Gilbert, J. C., Tsai, C.-T., Chiang, F.-T., Holden, T., Barney, N., & White, B. C. (2006) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2005.11.036 · Cited by 608

The Entire Regularization Path for the Support Vector Machine

Hastie, T. J., Rosset, S., Tibshirani, R. J., & Zhu, J. (2004) · Cited by 607

TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning

Olson, R. S., & Moore, J. H. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5_8 · Cited by 621

The properties of high-dimensional data spaces: implications for exploring gene and protein expression data

Clarke, R. C., Ressom, H. W., Wang, A., Xuan, J., Liu, M., Gehan, E. A., & Wang, Y. (2007) · Nature reviews. Cancer · doi:10.1038/nrc2294 · Cited by 625

Metabolomics: Current technologies and future trends

Hollywood, K. A., Brison, D. R., & Goodacre, R. (2006) · PROTEOMICS · doi:10.1002/pmic.200600106 · Cited by 608

Records of the Western Australian Museum

Karanovic, I. (2008) · eCite Digital Repository (University of Tasmania) · Cited by 605

Twenty Years of Mixture of Experts

Yuksel, S. E., Wilson, J. N., & Gader, P. D. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2200299 · Cited by 606

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

Yao, X., Tham, L. G., & Dai, F. C. (2008) · Geomorphology · doi:10.1016/j.geomorph.2008.02.011 · Cited by 607

Consistency of random forests

Scornet, E., Biau, G., & Vert, J.-P. (2015) · The Annals of Statistics · doi:10.1214/15-aos1321 · Cited by 612

Woody cover in African savannas: the role of resources, fire and herbivory.

Sankaran, M., Ratnam, J., & Hanan, N. (2008) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2007.00360.x · Cited by 589

Classification and Regression Trees, Bagging, and Boosting

Sutton, C. D. (2005) · Handbook of statistics · doi:10.1016/s0169-7161(04)24011-1 · Cited by 618

High-Dimensional Methods and Inference on Structural and Treatment Effects

Belloni, A., Chernozhukov, V., & Hansen, C. (2014) · Journal of Economic Perspectives · doi:10.1257/jep.28.2.29 · Cited by 722

A Proximal Stochastic Gradient Method with Progressive Variance Reduction

Xiao, L., & Zhang, T. (2014) · SIAM Journal on Optimization · doi:10.1137/140961791 · Cited by 623

Prediction Policy Problems

Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review · doi:10.1257/aer.p20151023 · Cited by 584

Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition

Faraway, J. J. (2016) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315382722 · Cited by 879

Mathematics for Machine Learning

Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108679930 · Cited by 616

An Empirical Comparison of Model Validation Techniques for Defect Prediction Models

Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2584050 · Cited by 574

Efficient Human Pose Estimation from Single Depth Images

Shotton, J., Girshick, R., Fitzgibbon, A. W., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., et al. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.241 · Cited by 590

Thalamic control of sensory selection in divided attention

Wimmer, R. D., Schmitt, L. I., Davidson, T. J., Nakajima, M., Deisseroth, K., & Halassa, M. M. (2015) · Nature · doi:10.1038/nature15398 · Cited by 530

Big data approaches to decomposing heterogeneity across the autism spectrum

Lombardo, M. V., Lai, M.-C., & Baron-Cohen, S. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0321-0 · Cited by 537

Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study

Triguero, I., García, S., & Herrera, F. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0706-y · Cited by 601

Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees

Leathwick, J., Elith, J., Francis, M., Hastie, T. J., & Taylor, P. (2006) · Marine Ecology Progress Series · doi:10.3354/meps321267 · Cited by 570

Adapting ranking SVM to document retrieval

Cao, Y., Xu, J., Liu, T., Li, H., Huang, Y., & Hon, H.-W. (2006) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1148170.1148205 · Cited by 576

Machine Learning Strategies for Time Series Forecasting

Bontempi, G., Taieb, S. B., & Borgne, Y. L. (2013) · Lecture notes in business information processing · doi:10.1007/978-3-642-36318-4_3 · Cited by 568

Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques

Fan, C., Xiao, F., & Wang, S. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.04.016 · Cited by 569

Deep double descent: where bigger models and more data hurt*

Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., & Sutskever, I. (2021) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/ac3a74 · Cited by 565

A survey of online failure prediction methods

Salfner, F., Lenk, M., & Malek, M. (2010) · ACM Computing Surveys · doi:10.1145/1670679.1670680 · Cited by 600

Diagnosing and responding to violations in the positivity assumption

Petersen, M. L., Porter, K. E., Gruber, S., Wang, Y., & van der Laan, M. J. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210386207 · Cited by 548

Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene

Buffa, F. M., Harris, A. L., West, C., & Miller, C. J. (2010) · British Journal of Cancer · doi:10.1038/sj.bjc.6605450 · Cited by 548

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Tuia, D., Volpi, M., Copa, L., Kanevski, M., & Muñoz-Marí, J. (2011) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2011.2139193 · Cited by 575

Step away from stepwise

Smith, G. (2018) · Journal Of Big Data · doi:10.1186/s40537-018-0143-6 · Cited by 607

A significance test for the lasso

Lockhart, R., Taylor, J., Tibshirani, R. J., & Tibshirani, R. (2014) · The Annals of Statistics · doi:10.1214/13-aos1175 · Cited by 541

Large Graph Construction for Scalable Semi-Supervised Learning

Liu, W., He, J., & Chang, S. (2010) · Cited by 554

arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

Hahsler, M., Grün, B., & Hornik, K. (2005) · Journal of Statistical Software · doi:10.18637/jss.v014.i15 · Cited by 589

Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases

Kell, D. B. (2009) · BMC Medical Genomics · doi:10.1186/1755-8794-2-2 · Cited by 554

Using data mining to predict secondary school student performance

Cortez, P. C., & Silva, A. M. G. (2008) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 558

Gradient forests: calculating importance gradients on physical predictors

Ellis, N., Smith, S. J., & Pitcher, C. R. (2011) · Ecology · doi:10.1890/11-0252.1 · Cited by 543

Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

Verrelst, J., Muñoz, J., Alonso, L., Delegido, J., Rivera-Caicedo, J. P., Camps‐Valls, G., & Moreno, J. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.11.002 · Cited by 570

Machine learning applications for building structural design and performance assessment: State-of-the-art review

Sun, H., Burton, H. V., & Huang, H. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101816 · Cited by 583

Predictably Unequal? The Effects of Machine Learning on Credit Markets

FUSTER, A., GOLDSMITH‐PINKHAM, P., RAMADORAI, T., & WALTHER, A. (2022) · The Journal of Finance · doi:10.1111/jofi.13090 · Cited by 872

Managing data transfers in computer clusters with orchestra

Chowdhury, M., Zaharia, M., Ma, J. H., Jordan, M. I., & Stoica, I. G. (2011) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2018436.2018448 · Cited by 547

Analytics for an Online Retailer: Demand Forecasting and Price Optimization

Ferreira, K., Lee, B. H. A., & Simchi‐Levi, D. (2015) · Manufacturing & Service Operations Management · doi:10.1287/msom.2015.0561 · Cited by 584

Unsupervised Learning With Random Forest Predictors

Shi, T., & Horvath, S. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x94072 · Cited by 557

Sparse Additive Models

Ravikumar, P., Lafferty, J., Liu, H., & Wasserman, L. (2009) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2009.00718.x · Cited by 548

Multiple dynamic representations in the motor cortex during sensorimotor learning

Huber, D., Gutnisky, D. A., Peron, S., O’Connor, D. H., Wiegert, J. S., Tian, L., Oertner, T. G., Looger, L. L., & Svoboda, K. (2012) · Nature · doi:10.1038/nature11039 · Cited by 540

Genomic selection: A paradigm shift in animal breeding

Meuwissen, T., Hayes, B., & Goddard, M. (2016) · Animal Frontiers · doi:10.2527/af.2016-0002 · Cited by 485

High-throughput classification of yeast mutants for functional genomics using metabolic footprinting

Allen, J., Davey, H. M., Broadhurst, D., Heald, J. K., Rowland, J. J., Oliver, S. G., & Kell, D. B. (2003) · Nature Biotechnology · doi:10.1038/nbt823 · Cited by 543

Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

Yılmaz, I. (2009) · Environmental Earth Sciences · doi:10.1007/s12665-009-0394-9 · Cited by 543

Joint Analysis of Functional Genomic Data and Genome-wide Association Studies of 18 Human Traits

Pickrell, J. K. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.03.004 · Cited by 529

Bioinformatics challenges for genome-wide association studies

Moore, J. H., Asselbergs, F. W., & Williams, S. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp713 · Cited by 540

Multivariate Analysis of Ecological Data Using CANOCO

(2005) · Bulletin of the Ecological Society of America · doi:10.1890/0012-9623(2005)86[201:maoedu]2.0.co;2 · Cited by 539

Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach

Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., & Cribb, M. C. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111221 · Cited by 540

Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis‐driven science in the post‐genomic era

Kell, D. B., & Oliver, S. G. (2004) · BioEssays · doi:10.1002/bies.10385 · Cited by 535

An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping

Heung, B., Ho, H. C., Zhang, J., Knudby, A. J., Bulmer, C., & Schmidt, M. (2015) · Geoderma · doi:10.1016/j.geoderma.2015.11.014 · Cited by 550

Learning photographic global tonal adjustment with a database of input / output image pairs

Bychkovsky, V., Paris, S., Chan, E., & Durand, F. (2011) · doi:10.1109/cvpr.2011.5995332 · Cited by 531

The promise of machine learning in predicting treatment outcomes in psychiatry

Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., et al. (2021) · World Psychiatry · doi:10.1002/wps.20882 · Cited by 531

Psychological factors associated with development of TMD: the OPPERA prospective cohort study

Fillingim, R. B., Ohrbach, R., Greenspan, J. D., Knott, C., Diatchenko, L., Dubner, R., Bair, E., Baraian, C., Mack, N., Slade, G. D., et al. (2013) · The Journal of Pain · doi:10.1016/j.jpain.2013.06.009 · Cited by 528

Evaluating the predictive performance of empirical estimators of natural mortality rate using information on over 200 fish species

Then, A. Y., Hoenig, J. M., Hall, N. G., Hewitt, D. A., & Jardim, H. E. E. (2014) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsu136 · Cited by 549

Economics in the age of big data

Liran Einav, B., & Levin, J. (2014) · Science · doi:10.1126/science.1243089 · Cited by 579

Climate and Wildfire in the Western United States

Westerling, A. L., Gershunov, A., Brown, T., Cayan, D. R., & Dettinger, M. D. (2003) · Bulletin of the American Meteorological Society · doi:10.1175/bams-84-5-595 · Cited by 543

An Introduction to Feature Extraction

Guyon, I., & Elisseeff, A. (2008) · Studies in fuzziness and soft computing · doi:10.1007/978-3-540-35488-8_1 · Cited by 571

Machine learning advances for time series forecasting

Masini, R., Medeiros, M. C., & Mendes, E. F. (2021) · Journal of Economic Surveys · doi:10.1111/joes.12429 · Cited by 601

Stability-Based Validation of Clustering Solutions

Lange, T., Röth, V., Braun, M. L., & Buhmann, J. M. (2004) · Neural Computation · doi:10.1162/089976604773717621 · Cited by 534

Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach

Hainmueller, J., & Hazlett, C. J. (2013) · Political Analysis · doi:10.1093/pan/mpt019 · Cited by 544

Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues

Chen, J. Y. C., & Barnes, M. J. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2013.2293535 · Cited by 554

Fault diagnosis based on Fisher discriminant analysis and support vector machines

Chiang, L. H., Kotanchek, M. E., & Kordon, A. K. (2003) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2003.10.002 · Cited by 531

Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy

Rossel, R. A. V., McGlynn, R. N., & McBratney, A. B. (2006) · Geoderma · doi:10.1016/j.geoderma.2006.07.004 · Cited by 535

Automated Classification and Analysis of Internet Malware

Bailey, M., Oberheide, J., Andersen, J. H., Mao, Z. M., Jahanian, F., & Nazario, J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74320-0_10 · Cited by 531

A Method of Automated Nonparametric Content Analysis for Social Science

Hopkins, D. J., & King, G. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2009.00428.x · Cited by 765

Flow Clustering Using Machine Learning Techniques

McGregor, A., Hall, M., Lorier, P., & Brunskill, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24668-8_21 · Cited by 523

Functional gradients of the cerebellum

Guell, X., Schmahmann, J. D., Gabrieli, J. D., & Ghosh, S. S. (2018) · eLife · doi:10.7554/elife.36652 · Cited by 472

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

Forkuor, G., Hounkpatin, O., Welp, G., & Thiel, M. A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170478 · Cited by 541

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Xing, F., & Yang, L. (2016) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2016.2515127 · Cited by 527

The auxin signalling network translates dynamic input into robust patterning at the shoot apex

Vernoux, T., Brunoud, G., Farcot, E., Morin, V., Van den Daele, H., Legrand, J., Oliva, M., Das, P., Larrieu, A., Wells, D., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.39 · Cited by 576

Automatic mood detection and tracking of music audio signals

Lu, L., Liu, D., & Zhang, H.-J. (2005) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tsa.2005.860344 · Cited by 528

Machine learning bandgaps of double perovskites

Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., & Lookman, T. (2016) · Scientific Reports · doi:10.1038/srep19375 · Cited by 518

Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models

Austin, P. C., & Steyerberg, E. W. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280214558972 · Cited by 516

Who voted for Brexit? A comprehensive district-level analysis

Becker, S. O., Fetzer, T., & Novy, D. (2017) · Economic Policy · doi:10.1093/epolic/eix012 · Cited by 647

What is Machine Learning? A Primer for the Epidemiologist

Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019) · American Journal of Epidemiology · doi:10.1093/aje/kwz189 · Cited by 584

Applications of machine learning in animal behaviour studies

Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2016.12.005 · Cited by 505

Ensembles of Deep LSTM Learners for Activity Recognition using Wearables

Guan, Y., & Plötz, T. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3090076 · Cited by 511

Empirical Processes in M-Estimation

van de Geer, S. (2000) · Cited by 530

Category-Specific Video Summarization

Potapov, D., Douze, M., Harchaoui, Z., & Schmid, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_35 · Cited by 521

The effect of including C-reactive protein in cardiovascular risk prediction models for women

Cook, N. R., Buring, J. E., & Ridker, P. M. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-145-1-200607040-00128 · Cited by 504

The Elements of Statistical Learning -- Data Mining, . . .

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The Elements of Statistical Learning: Data Mining, Inference and Prediction. By

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The Elements of Statistical Learning: Data Mining, Inference, and Prediction by HASTIE, T., TIBSHIRANI, R., and FRIEDMAN, J.

Rosa, G. J. M. (2010) · Biometrics · doi:10.1111/j.1541-0420.2010.01516.x · Cited by 31

An Introduction to Statistical Learning

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106

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