The Elements of Statistical Learning

Sensory and decision-related activity propagate in a cortical feedback loop during touch perception

Kwon, S. E., Yang, H., Minamisawa, G., & O'Connor, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4356 · Cited by 210

Theoretical, Statistical, and Practical Perspectives on Pattern-based Classification Approaches to the Analysis of Functional Neuroimaging Data

O’Toole, A. J., Jiang, F., Abdi, H., Pénard, N., Dunlop, J. P., & Parent, M. A. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.11.1735 · Cited by 245

Detecting and Localizing Wireless Spoofing Attacks

Chen, Y. J., Trappe, W. K., & Martin, R. P. (2007) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2007.4292831 · Cited by 243

Selecting the number of components in principal component analysis using cross-validation approximations

Josse, J., & Husson, F. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.11.012 · Cited by 236

Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions

Greenland, S., & Mansournia, M. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6537 · Cited by 237

The priority of prediction in ecological understanding

Houlahan, J. E., McKinney, S. T., Anderson, T. M., & McGill, B. J. (2016) · Oikos · doi:10.1111/oik.03726 · Cited by 243

Review: Advances in groundwater potential mapping

Díaz-Alcaide, S., & Martínez‐Santos, P. (2019) · Hydrogeology Journal · doi:10.1007/s10040-019-02001-3 · Cited by 247

Review for order reduction based on proper orthogonal decomposition and outlooks of applications in mechanical systems

Lu, K., Jin, Y., Chen, Y., Yang, Y., Hou, L., Zhang, Z., Li, Z., & Fu, C. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.01.018 · Cited by 235

Class prediction for high-dimensional class-imbalanced data

Blagus, R., & Lusa, L. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-523 · Cited by 241

An Introduction to Bootstrap Methods with Applications to R

Chernick, M. R., & LaBudde, R. A. (2011) · ODU Digital Commons (Old Dominion University) · Cited by 242

Electro-dewatering of wastewater sludge: Influence of the operating conditions and their interactions effects

Mahmoud, A., Olivier, J., Vaxelaire, J., & Hoadley, A. (2011) · Water Research · doi:10.1016/j.watres.2011.02.029 · Cited by 235

Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods

Borra, S., & Ciaccio, A. D. (2010) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2010.03.004 · Cited by 236

Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models

Visani, G., Bagli, E., Chesani, F., Poluzzi, A., & Capuzzo, D. (2021) · Journal of the Operational Research Society · doi:10.1080/01605682.2020.1865846 · Cited by 274

A review of the F-measure: its history, properties, criticism, and alternatives

Christen, P., Hand, D. J., & Kirielle, N. (2024) · ACM Computing Surveys · doi:10.1145/3606367 · Cited by 236

Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions

Heilman, M., & Smith, N. A. (2018) · doi:10.1184/r1/6473795 · Cited by 234

The Research Diagnostic Criteria for Temporomandibular Disorders. V: methods used to establish and validate revised Axis I diagnostic algorithms.

SCHIFFMAN, E. L., Ohrbach, R. K., Truelove, E. L., Tai, F., Anderson, G. C., Pan, W., Gonzalez, Y. M., John, M. T., Sommers, E. E., List, T., et al. (2010) · PubMed · Cited by 234

Locally Weighted Censored Quantile Regression

WANG, H. J., & Wang, L. (2009) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.tm08230 · Cited by 238

Assessing the predictive performance of structural equation model estimators

Evermann, J., & Tate, M. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2016.03.050 · Cited by 252

Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters

Ayad, H. G., & Kamel, M. S. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1138 · Cited by 233

Causal Models and Learning from Data

Petersen, M. L., & van der Laan, M. J. (2014) · Epidemiology · doi:10.1097/ede.0000000000000078 · Cited by 262

A methodology to derive global maps of leaf traits using remote sensing and climate data

Moreno‐Martínez, Á., Camps‐Valls, G., Kattge, J., Robinson, N., Reichstein, M., van Bodegom, P. M., Krämer, K., Cornelissen, J. H. C., Reich, P. B., Bahn, M., et al. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.09.006 · Cited by 242

Identifying Pb-free perovskites for solar cells by machine learning

Im, J., Lee, S., Ko, T.-W., Kim, H. W., Hyon, Y., & Chang, H. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0177-0 · Cited by 239

Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews

Lucini, F. R., Tonetto, L. M., Fogliatto, F. S., & Anzanello, M. J. (2019) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2019.101760 · Cited by 254

Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data

Xi, N. M., & Li, J. J. (2021) · Cell Systems · doi:10.1016/j.cels.2020.11.008 · Cited by 208

Making sense of global sensitivity analyses

Wainwright, H. M., Finsterle, S. A., Jung, Y., Zhou, Q., & Birkhölzer, J. (2013) · Computers & Geosciences · doi:10.1016/j.cageo.2013.06.006 · Cited by 245

Spread of SARS-CoV-2 Coronavirus likely constrained by climatePreprint

Araújo, M. B., & Naimi, B. (2020) · medRxiv · doi:10.1101/2020.03.12.20034728 · Cited by 265

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding

van Eeuwijk, F. A., Bustos-Korts, D., Millet, E. J., Boer, M. P., Kruijer, W., Thompson, A., Malosetti, M., Iwata, H., Quiroz, R., Kuppe, C., et al. (2019) · Plant Science · doi:10.1016/j.plantsci.2018.06.018 · Cited by 217

Classification of microarray data using gene networks

Rapaport, F., Zinovyev, A. Y., Dutreix, M., Barillot, E., & Vert, J. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-35 · Cited by 240

18-Month Predictors of Later Outcomes in Younger Siblings of Children With Autism Spectrum Disorder: A Baby Siblings Research Consortium Study

Chawarska, K., Shic, F., Macari, S. L., Campbell, D. J., Brian, J. A., Landa, R. J., Hutman, T., Nelson, C. A., Ozonoff, S. J., Tager-Flusberg, H. B., et al. (2014) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2014.09.015 · Cited by 229

A survey of functional principal component analysis

Shang, H. L. (2013) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-013-0213-1 · Cited by 230

Analytical Variance-Based Global Sensitivity Analysis in Simulation-Based Design Under Uncertainty

Chen, W., Jin, R., & Sudjianto, A. (2004) · Journal of Mechanical Design · doi:10.1115/1.1904642 · Cited by 230

Modeling shark bycatch: The zero-inflated negative binomial regression model with smoothing

Minami, M., Lennert‐Cody, C. E., Gao, W., & Román, M. (2006) · Fisheries Research · doi:10.1016/j.fishres.2006.10.019 · Cited by 238

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

Pérez‐Rodríguez, P., Gianola, D., González‐Camacho, J. M., Crossa, J. L., Manès, Y., & Dreisigacker, S. (2012) · G3 Genes Genomes Genetics · doi:10.1534/g3.112.003665 · Cited by 236

Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods

Shokrzadeh, S., Jozani, M. J., & Bibeau, E. (2014) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2014.2345059 · Cited by 230

PLS-SEM for Software Engineering Research: An Introduction and Survey

Russo, D., & Stol, K.-J. (2021) · ACM Computing Surveys · doi:10.1145/3447580 · Cited by 290

Machine Learning in Nanoscience: Big Data at Small Scales

Brown, K. A., Brittman, S., Maccaferri, N., Jariwala, D., & Celano, U. (2020) · Nano Letters · doi:10.1021/acs.nanolett.9b04090 · Cited by 229

Interval Tuning in the Primate Medial Premotor Cortex as a General Timing Mechanism

Merchant, H., Pérez, O., Zarco, W., & Gámez, J. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.5513-12.2013 · Cited by 229

Variability-aware performance prediction: A statistical learning approach

Guo, J., Czarnecki, K., Apel, S., Siegmund, N., & Wąsowski, A. (2013) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2013.6693089 · Cited by 229

Computer‐Assisted Keyword and Document Set Discovery from Unstructured Text

King, G., Lam, P., & Roberts, M. E. (2017) · American Journal of Political Science · doi:10.1111/ajps.12291 · Cited by 216

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions

Lo-Ciganic, W.-H., Huang, J. L., Zhang, H. H., Weiss, J. C., Wu, Y., Kwoh, C. K., Donohue, J. M., Cochran, G., Gordon, A. J., Malone, D. C., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.0968 · Cited by 228

Multivariate Data Analysis and Machine Learning in Alzheimer's Disease with a Focus on Structural Magnetic Resonance Imaging

Falahati, F., Westman, E., & Simmons, A. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-131928 · Cited by 245

Causal inference and developmental psychology.

Foster, E. M. (2010) · Developmental Psychology · doi:10.1037/a0020204 · Cited by 241

Does pollen-assemblage richness reflect floristic richness? A review of recent developments and future challenges

Birks, H. J. B., Felde, V. A., Bjune, A. E., Grytnes, J., Seppä, H., & Giesecke, T. (2016) · Review of Palaeobotany and Palynology · doi:10.1016/j.revpalbo.2015.12.011 · Cited by 236

Research Review: Environmental exposures, neurodevelopment, and child mental health – new paradigms for the study of brain and behavioral effects

Rauh, V., & Margolis, A. E. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12537 · Cited by 247

Data Mining: Concepts and Techniques

Yang, H. (2008) · Cited by 228

Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion

Garosi, Y., Sheklabadi, M., Conoscenti, C., Pourghasemi, H. R., & Oost, K. V. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.02.093 · Cited by 230

An empirical comparison of machine learning techniques for dam behaviour modelling

Salazar, F., Toledo, M. A., Oñate, E., & Morán, R. (2015) · Structural Safety · doi:10.1016/j.strusafe.2015.05.001 · Cited by 225

Predicting build failures using social network analysis on developer communication

Wolf, T., Schröter, A., Damian, D., & Nguyen, T. (2009) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2009.5070503 · Cited by 226

Example-dependent cost-sensitive decision trees

Bahnsen, A. C., Aouada, D., & Ottersten, B. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.04.042 · Cited by 252

Asynchronous Distributed ADMM for Large-Scale Optimization—Part I: Algorithm and Convergence Analysis

Chang, T., Hong, M., Liao, W.-C., & Wang, X. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2537271 · Cited by 226

Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach

Moro, S., Rita, P., & Vala, B. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2016.02.010 · Cited by 226

Machine learning for combustion

Zhou, L., Song, Y., Ji, W., & Wei, H. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100128 · Cited by 246

CAM: Causal additive models, high-dimensional order search and penalized regression

Bühlmann, P., Peters, J., & Ernest, J. (2014) · The Annals of Statistics · doi:10.1214/14-aos1260 · Cited by 266

Shining Light on the Microbial World

Huang, W. E., Li, M., Jarvis, R. M., Goodacre, R., & Banwart, S. A. (2010) · Advances in applied microbiology · doi:10.1016/s0065-2164(10)70005-8 · Cited by 230

Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist

Sengupta, P. P., Shrestha, S., Berthon, B., Messas, E., Donal, E., Tison, G. H., Min, J. K., D’hooge, J., Voigt, J., Dudley, J. T., et al. (2020) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2020.07.015 · Cited by 236

AI-powered landslide susceptibility assessment in Hong Kong

Wang, H., Zhang, L. M., Luo, H., He, J., & Cheung, R. W. M. (2021) · Engineering Geology · doi:10.1016/j.enggeo.2021.106103 · Cited by 232

Conditional validity of inductive conformal predictors

Vovk, V. (2013) · Machine Learning · doi:10.1007/s10994-013-5355-6 · Cited by 225

TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization

Miché, Y., van Heeswijk, M., Bas, P., Simula, O., & Lendasse, A. (2011) · Neurocomputing · doi:10.1016/j.neucom.2010.12.042 · Cited by 224

Prediction error identification of linear systems: A nonparametric Gaussian regression approach

Pillonetto, G., Chiuso, A., & Nicolao, G. D. (2010) · Automatica · doi:10.1016/j.automatica.2010.11.004 · Cited by 221

Vision Survival after Open Globe Injury Predicted by Classification and Regression Tree Analysis

Schmidt, G. W., Broman, A. T., Hindman, H. B., & Grant, M. P. (2007) · Ophthalmology · doi:10.1016/j.ophtha.2007.04.008 · Cited by 222

Modeling soil depth from topographic and land cover attributes

Tesfa, T. K., Tarboton, D. G., Chandler, D. G., & McNamara, J. P. (2009) · Water Resources Research · doi:10.1029/2008wr007474 · Cited by 240

A Review of Machine Learning Applications to Coastal Sediment Transport and MorphodynamicsPreprint

Goldstein, E. B., Coco, G., & Plant, N. G. (2018) · doi:10.1016/j.earscirev.2019.04.022 · Cited by 236

Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale

Mishra, U., Lal, R., Liu, D., & Meirvenne, M. V. (2010) · Soil Science Society of America Journal · doi:10.2136/sssaj2009.0158 · Cited by 223

A Systems Biology Strategy Reveals Biological Pathways and Plasma Biomarker Candidates for Potentially Toxic Statin-Induced Changes in Muscle

Laaksonen, R., Katajamaa, M., Päivä, H., Sysi-Aho, M., Saarinen, L., Junni, P., Lütjohann, D., Smet, J., Van Coster, R., Seppänen-Laakso, T., et al. (2006) · PLoS ONE · doi:10.1371/journal.pone.0000097 · Cited by 223

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

Howard, R., Carriquiry, A. L., & Beavis, W. D. (2014) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.010298 · Cited by 227

Clinical Orofacial Characteristics Associated With Risk of First-Onset TMD: The OPPERA Prospective Cohort Study

Ohrbach, R. K., Bair, E., Fillingim, R. B., Gonzalez, Y. M., Gordon, S. M., Lim, P. F., Ribeiro-Dasilva, M., Diatchenko, L. B., Dubner, R. A., Greenspan, J. D., et al. (2013) · Journal of Pain · doi:10.1016/j.jpain.2013.07.018 · Cited by 232

Markov clustering versus affinity propagation for the partitioning of protein interaction graphs

Vlasblom, J., & Wodak, S. J. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-99 · Cited by 223

Contextual encoding by ensembles of medial prefrontal cortex neurons

Hyman, J. M., Ma, L., Balaguer‐Ballester, E., Durstewitz, D., & Seamans, J. K. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114415109 · Cited by 241

A regionalized national universal kriging model using Partial Least Squares regression for estimating annual PM2.5 concentrations in epidemiology

Sampson, P. D., Richards, M., Szpiro, A. A., Bergen, S. R., Sheppard, L. A., Larson, T. V., & Kaufman, J. D. (2013) · Atmospheric Environment · doi:10.1016/j.atmosenv.2013.04.015 · Cited by 222

COMPONENTS OF UNCERTAINTY IN SPECIES DISTRIBUTION ANALYSIS: A CASE STUDY OF THE GREAT GREY SHRIKE

Dormann, C. F., Purschke, O., Márquez, J. R. G., Lautenbach, S., & Schröder, B. (2008) · Ecology · doi:10.1890/07-1772.1 · Cited by 222

Authorship Attribution for Social Media Forensics

Rocha, A., Scheirer, W. J., Forstall, C. W., Cavalcante, T., Theophilo, A., Shen, B., Carvalho, A. R. B., & Stamatatos, E. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2603960 · Cited by 222

A comparison of six metamodeling techniques applied to building performance simulations

Østergård, T., Jensen, R. L., & Maagaard, S. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.10.102 · Cited by 222

Matching and Record Linkage

Winkler, W. E. (1995) · Wiley series in probability and statistics · doi:10.1002/9781118150504.ch20 · Cited by 222

Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool

Cortez, P. C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14400-4_44 · Cited by 221

Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages

Standaert, F., & Archambeau, C. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85053-3_26 · Cited by 221

Large-scale machine learning at twitter

Lin, J., & Kolcz, A. (2012) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2213836.2213958 · Cited by 231

Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery

Liu, H., Palatucci, M., & Zhang, J. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553458 · Cited by 221

Supervised Learning of Quantizer Codebooks by Information Loss Minimization

Lazebnik, S., & Raginsky, M. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.138 · Cited by 226

Gaining insights from social media language: Methodologies and challenges.

Kern, M. L., Park, G., Eichstaedt, J. C., Schwartz, H. A., Sap, M., Smith, L. K., & Ungar, L. H. (2016) · Psychological Methods · doi:10.1037/met0000091 · Cited by 221

Corporate bankruptcy prediction: a high dimensional analysis

Jones, S. (2017) · Review of Accounting Studies · doi:10.1007/s11142-017-9407-1 · Cited by 214

Forecasting Corn Yield with Machine Learning Ensembles

Shahhosseini, M., Hu, G., & Archontoulis, S. V. (2020) · Iowa State University Digital Repository (Iowa State University) · Cited by 221

Machine learning for large‐scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures

Kubota, K. J., Chen, J. A., & Little, M. A. (2016) · Movement Disorders · doi:10.1002/mds.26693 · Cited by 210

Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty

Ryali, S., Chen, T., Supekar, K., & Menon, V. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.054 · Cited by 220

Clustering and variance maps for cryo-electron tomography using wedge-masked differences

Heumann, J. M., Hoenger, A., & Mastronarde, D. N. (2011) · Journal of Structural Biology · doi:10.1016/j.jsb.2011.05.011 · Cited by 220

Machine Learning Methods for Demand Estimation

Bajari, P., Denis Nekipelov, C., Ryan, S., & Yang, M. (2015) · American Economic Review · doi:10.1257/aer.p20151021 · Cited by 231

Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment

Va, V., Choi, J. J., Shimizu, T., Bansal, G., & Heath, R. W. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2787627 · Cited by 226

Transforming data into knowledge for improved wastewater treatment operation: A critical review of techniques

Corominas, L., Garrido‐Baserba, M., Villez, K., Olsson, G., Cortés, U., & Manel Poch, A. (2017) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2017.11.023 · Cited by 220

Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning

Lyngdoh, G. A., Zaki, M. F. M., Krishnan, N. M. A., & Das, S. (2022) · Cement and Concrete Composites · doi:10.1016/j.cemconcomp.2022.104414 · Cited by 234

Assessing the incremental value of diagnostic and prognostic markers: a review and illustration

Steyerberg, E. W., Pencina, M. J., Lingsma, H. F., Kattan, M. W., Vickers, A. J., & Van Calster, B. (2012) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2011.02562.x · Cited by 219

CpG Island Methylation in Human Lymphocytes Is Highly Correlated with DNA Sequence, Repeats, and Predicted DNA Structure

Bock, C., Paulsen, M., Tierling, S., Mikeska, T., Lengauer, T., & Walter, J. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020026 · Cited by 223

Machine learning in agricultural and applied economics

Storm, H., Baylis, K., & Heckelei, T. (2019) · European Review of Agricultural Economics · doi:10.1093/erae/jbz033 · Cited by 243

A researcher's guide to regression, discretization, and median splits of continuous variables

Rucker, D., McShane, B., & Preacher, K. J. (2015) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2015.04.004 · Cited by 221

Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning

Vogelstein, J. T., Park, Y., Ohyama, T., Kerr, R. A., Truman, J. W., Priebe, C. E., & Zlatic, M. (2014) · Science · doi:10.1126/science.1250298 · Cited by 218

A framework for knowledge discovery in massive building automation data and its application in building diagnostics

Fan, C., Xiao, F., & Yan, C. (2014) · Automation in Construction · doi:10.1016/j.autcon.2014.12.006 · Cited by 218

Overfitting in prediction models – Is it a problem only in high dimensions?

Subramanian, J., & Simon, R. M. (2013) · Contemporary Clinical Trials · doi:10.1016/j.cct.2013.06.011 · Cited by 218

Radius Margin Bounds for Support Vector Machines with the RBF Kernel

Chung, K.-M., Kao, W.-C., Sun, C., Wang, L.-L., & Lin, C. (2003) · Neural Computation · doi:10.1162/089976603322385108 · Cited by 218

The Spoils of Victory: Campaign Donations and Government Contracts in Brazil

Boas, T. C., Hidalgo, F. D., & Richardson, N. P. (2014) · The Journal of Politics · doi:10.1017/s002238161300145x · Cited by 218

Maximum Likelihood, Profile Likelihood, and Penalized Likelihood: A Primer

Cole, S. R., Chu, H., & Greenland, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt245 · Cited by 229

Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

Zhu, X., Suk, H., Lee, S., & Shen, D. G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2466616 · Cited by 217

Mechanical properties of Austenitic Stainless Steel 304L and 316L at elevated temperatures

Desu, R. K., Hansoge, N. K., Balu, A., Gupta, A. K., & Singh, S. K. (2015) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2015.04.001 · Cited by 218

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

Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2013)

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

Beezer, R. A., Hastie, T., Tibshirani, R., & Springer, J. F. (2002)

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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