The Elements of Statistical Learning

Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection

Teipel, S. J., Drzezga, A. E., Grothe, M. J., Barthel, H., Chételat, G., Schuff, N. W., Skudlarski, P., Cavedo, E., Frisoni, G. B., Hoffmann, W. U., et al. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(15)00093-9 · Cited by 286

The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project

Rossel, R. A. V., Chen, C. R., Grundy, M., Searle, R., Clifford, D. J., & Campbell, P. H. (2015) · Soil Research · doi:10.1071/sr14366 · Cited by 274

Temporal properties of low power wireless links

Cerpa, A., Wong, J. L., Potkonjak, M., & Estrin, D. L. (2005) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/1062689.1062741 · Cited by 273

Shallow neural networks for fluid flow reconstruction with limited sensors

Erichson, N. B., Mathelin, L., Yao, Z., Brunton, S. L., Mahoney, M. W., & Kutz, J. N. (2020) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2020.0097 · Cited by 259

Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress

Lu, Y., Saeys, W., Kim, M. J., Peng, Y., & Lu, R. (2020) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2020.111318 · Cited by 278

Biomarker Discovery and Validation: Statistical Considerations

Ou, F., Michiels, S., Shyr, Y., Adjei, A. A., & Oberg, A. L. (2021) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2021.01.1616 · Cited by 292

Autism and mental retardation among offspring born after in vitro fertilization

Sandin, S., Nygren, K.-G., Iliadou, A., Hultman, C. M., & Reichenberg, A. (2013) · JAMA · doi:10.1001/jama.2013.7222 · Cited by 260

Explaining Data-Driven Document Classifications1

Martens, D., & Provost, F. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.04 · Cited by 264

Personality and Longevity: Knowns, Unknowns, and Implications for Public Health and Personalized Medicine

Chapman, B. P., Roberts, B., & Duberstein, P. (2011) · Journal of Aging Research · doi:10.4061/2011/759170 · Cited by 283

Evaluation of the lasso and the elastic net in genome-wide association studies

Waldmann, P., Mészáros, G., Gredler, B., Fuerst, C., & Sölkner, J. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00270 · Cited by 270

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Brokamp, C., Jandarov, R., Rao, M. B., LeMasters, G. K., & Ryan, P. (2016) · Atmospheric Environment · doi:10.1016/j.atmosenv.2016.11.066 · Cited by 264

Sparse logistic regression for whole-brain classification of fMRI data

Ryali, S., Supekar, K., Abrams, D. A., & Menon, V. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.040 · Cited by 272

Exosomal microRNA signatures in multiple sclerosis reflect disease status

Ebrahimkhani, S., Vafaee, F., Young, P. E., Hur, S. S. J., Hawke, S., Devenney, E., Beadnall, H., Barnett, M. H., Suter, C. M., & Buckland, M. E. (2017) · Scientific Reports · doi:10.1038/s41598-017-14301-3 · Cited by 247

A Survey on Gaussian Processes for Earth-Observation Data Analysis: A Comprehensive Investigation

Camps‐Valls, G., Verrelst, J., Muñoz-Marí, J., Laparra, V., Mateo, F., & Gomez-Dans, J. L. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2015.2510084 · Cited by 272

Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study

McConnell, M. V., Shcherbina, A., Pavlovic, A., Homburger, J. R., Goldfeder, R. L., Waggot, D., Cho, M. K., Rosenberger, M. E., Haskell, W. L., Myers, J., et al. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.4395 · Cited by 239

Evaluating the Classification of Economic Activity into Recessions and Expansions

Berge, T. J., & Jordà, Ò. (2011) · American Economic Journal Macroeconomics · doi:10.1257/mac.3.2.246 · Cited by 254

Hierarchical Clustering of Time-Series Data Streams

Rodrigues, P. P., da Gama, J. M. P., & Pedroso, J. P. (2008) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.190727 · Cited by 261

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2022) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 259

Parkinson’s Disease Diagnosis Using Machine Learning and Voice

Wroge, T. J., Özkanca, Y., Demiroğlu, C., Si, D., Atkins, D. C., & Ghomi, R. H. (2018) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb.2018.8615607 · Cited by 261

Adaptive Piecewise Polynomial Estimation via Trend Filtering

Tibshirani, R. J. (2013) · Cited by 260

What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from Big data analytics

Kushwaha, A. K., Kumar, P., & Kar, A. K. (2021) · Industrial Marketing Management · doi:10.1016/j.indmarman.2021.08.011 · Cited by 308

Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy

Ballabio, C., & Sterlacchini, S. (2012) · Mathematical Geosciences · doi:10.1007/s11004-011-9379-9 · Cited by 266

Data mining in building automation system for improving building operational performance

Xiao, F., & Fan, C. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.02.005 · Cited by 258

Model-based boosting in R: a hands-on tutorial using the R package mboost

Hofner, B., Mayr, A. J., Robinzonov, N., & Schmid, M. C. (2012) · Computational Statistics · doi:10.1007/s00180-012-0382-5 · Cited by 268

The Data Revolution and Economic Analysis

Liran Einav, B., & Levin, J. (2014) · Innovation Policy and the Economy · doi:10.1086/674019 · Cited by 258

Context-for-wireless

Rahmati, A., & Zhong, L. (2007) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/1247660.1247681 · Cited by 262

The production of prediction: What does machine learning want?

Mackenzie, A. (2015) · European Journal of Cultural Studies · doi:10.1177/1367549415577384 · Cited by 283

Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm

Zhan, Y., Luo, Y., Deng, X., Chen, H., Grieneisen, M. L., Shen, X., Zhu, L., & Zhang, M. (2017) · Atmospheric Environment · doi:10.1016/j.atmosenv.2017.02.023 · Cited by 258

Deep learning classification of lung cancer histology using CT images

Chaunzwa, T. L., Hosny, A., Xu, Y., Shafer, A., Diao, N., Lanuti, M. D., Christiani, D. C., Mak, R. H., & Aerts, H. J. W. L. (2021) · Scientific Reports · doi:10.1038/s41598-021-84630-x · Cited by 278

RE-EM trees: a data mining approach for longitudinal and clustered data

Sela, R. J., & Simonoff, J. S. (2011) · Machine Learning · doi:10.1007/s10994-011-5258-3 · Cited by 274

Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China

Xu, C. B., Xu, X., Dai, F., & Saraf, A. K. (2012) · Computers & Geosciences · doi:10.1016/j.cageo.2012.01.002 · Cited by 257

Colonic Volvulus in the United States

Halabi, W. J., Jafari, M. D., Kang, C. Y., Nguyen, V. Q., Carmichael, J. C., Mills, S., Pigazzi, A., & Stamos, M. J. (2013) · Annals of Surgery · doi:10.1097/sla.0b013e31828c88ac · Cited by 277

Earthquake magnitude prediction in Hindukush region using machine learning techniques

Asim, K. M., Alvarez, F. M., Basit, A., & Iqbal, T. (2016) · Natural Hazards · doi:10.1007/s11069-016-2579-3 · Cited by 257

Multiobjective Optimization in Bioinformatics and Computational Biology

Handl, J., Kell, D. B., & Knowles, J. (2007) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.070203 · Cited by 308

Beyond Missing Heritability: Prediction of Complex Traits

Makowsky, R., Pajewski, N. M., Klimentidis, Y. C., Vazquez, A. I., Duarte, C. W., Allison, D. B., & de los Campos, G. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002051 · Cited by 254

High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures

Selige, T., Böhner, J., & Schmidhalter, U. (2006) · Geoderma · doi:10.1016/j.geoderma.2006.03.050 · Cited by 258

An ensemble learning framework for anomaly detection in building energy consumption

Araya, D. B., Grolinger, K., ElYamany, H. F., Capretz, M. A. M., & Bitsuamlak, G. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.02.058 · Cited by 263

Modern Multivariate Statistical Techniques: Regression, Classification and Manifold Learning

Maindonald, J. H. (2009) · Journal of Statistical Software · doi:10.18637/jss.v029.b11 · Cited by 256

A non-antibiotic-disrupted gut microbiome is associated with clinical responses to CD19-CAR-T cell cancer immunotherapy

Stein-Thoeringer, C. K., Saini, N. Y., Zamir, E., Blumenberg, V., Schubert, M.-L., Mor, U., Fante, M. A., Schmidt, S., Hayase, E., Hayase, T., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02234-6 · Cited by 255

Real‐time fMRI using brain‐state classification

LaConte, S. M., Peltier, S., & Hu, X. P. (2006) · Human Brain Mapping · doi:10.1002/hbm.20326 · Cited by 259

Computer age statistical inference algorithms, evidence, and data science

Efron, B., & Hastie, T. J. (2016) · Thammasat University Digital Collections · Cited by 263

Kynurenine pathway metabolomics predicts and provides mechanistic insight into multiple sclerosis progression

Lim, C. K., BILGIN, A. A. B., Lovejoy, D. B., Tan, V. X., Bustamante, S., Taylor, B. V., Bessede, A., Brew, B. J., & Guillemin, G. J. (2017) · Scientific Reports · doi:10.1038/srep41473 · Cited by 262

What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence

Lan, Q., Wen, D., Zhang, Z., Zeng, Q., Chen, X., Popovski, P., & Huang, K. (2021) · Journal of Communications and Information Networks · doi:10.23919/jcin.2021.9663101 · Cited by 285

Machine learning: A primer

Bzdok, D., Krzywinski, M., & Altman, N. (2017) · Nature Methods · doi:10.1038/nmeth.4526 · Cited by 246

Spatiotemporal exploratory models for broad‐scale survey data

Fink, D., Hochachka, W. M., Zuckerberg, B., Winkler, D. W., Shaby, B., Munson, M. A., Hooker, G., Riedewald, M., Sheldon, D. R., & Kelling, S. (2010) · Ecological Applications · doi:10.1890/09-1340.1 · Cited by 254

Robust Wasserstein profile inference and applications to machine learning

Blanchet, J., Kang, Y., & Murthy, K. (2019) · Journal of Applied Probability · doi:10.1017/jpr.2019.49 · Cited by 254

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection

Yasui, Y. (2003) · Biostatistics · doi:10.1093/biostatistics/4.3.449 · Cited by 249

Machine Learning Techniques for Optimizing Design of Double T-Shaped Monopole Antenna

Sharma, Y., Zhang, H. H., & Xin, H. (2020) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2020.2966051 · Cited by 265

Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach

Chen, X. (., Zahiri, M., & Zhang, S. (2017) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2016.12.018 · Cited by 252

Preserving Statistical Validity in Adaptive Data Analysis

Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., & Roth, A. (2015) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2746539.2746580 · Cited by 294

Representation of Spatial Goals in Rat Orbitofrontal Cortex

Feierstein, C. E., Quirk, M. C., Uchida, N., Sosulski, D. L., & Mainen, Z. F. (2006) · Neuron · doi:10.1016/j.neuron.2006.06.032 · Cited by 257

Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks

Reiss, D. J., Baliga, N. S., & Bonneau, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-280 · Cited by 257

Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy

Firmino, M., Angelo, G., de Morais, A. H. F., Ribeiro-Dantas, M. D. C., & Valentim, R. A. D. M. (2016) · BioMedical Engineering OnLine · doi:10.1186/s12938-015-0120-7 · Cited by 256

Investigating Conversational Dynamics: Interactive Alignment, Interpersonal Synergy, and Collective Task Performance

Fusaroli, R., & Tylén, K. (2015) · Cognitive Science · doi:10.1111/cogs.12251 · Cited by 273

Machine learning attacks against the Asirra CAPTCHA

Golle, P. (2008) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1455770.1455838 · Cited by 253

Testing machine learning based systems: a systematic mapping

Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M., & Tonella, P. (2020) · Empirical Software Engineering · doi:10.1007/s10664-020-09881-0 · Cited by 251

Single-Unit Stability Using Chronically Implanted Multielectrode Arrays

Dickey, A. S., Suminski, A., Amit, Y., & Hatsopoulos, N. G. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90920.2008 · Cited by 234

Fast sparse regression and classification

Friedman, J. H. (2012) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2012.05.001 · Cited by 251

A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs

Al-Anazi, A. F., & Gates, I. D. (2010) · Engineering Geology · doi:10.1016/j.enggeo.2010.05.005 · Cited by 256

A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction

Spooner, A., Chen, E., Sowmya, A., Sachdev, P., Kochan, N. A., Trollor, J., & Brodaty, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-77220-w · Cited by 284

Condition based maintenance: a survey

Prajapati, A., Bechtel, J. E., & Ganesan, S. (2012) · Journal of Quality in Maintenance Engineering · doi:10.1108/13552511211281552 · Cited by 273

Can we predict customer lifetime value?

Malthouse, E. C., & Blattberg, R. C. (2005) · Journal of Interactive Marketing · doi:10.1002/dir.20027 · Cited by 248

Attribute selection with fuzzy decision reducts

Chris Cornelis, A., Jensen, R., Hurtado, G. A. P., & Ślȩzak, D. (2009) · Information Sciences · doi:10.1016/j.ins.2009.09.008 · Cited by 250

Prioritizing Influential Factors for Freeway Incident Clearance Time Prediction Using the Gradient Boosting Decision Trees Method

Ma, X., Ding, C., Luan, S., Wang, Y., & Wang, Y. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2635719 · Cited by 248

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

Fox, E. W., Hill, R. A., Leibowitz, S. G., Olsen, A. R., Thornbrugh, D. J., & Weber, M. H. (2017) · Environmental Monitoring and Assessment · doi:10.1007/s10661-017-6025-0 · Cited by 272

A data-driven statistical model for predicting the critical temperature of a superconductor

Hamidieh, K. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.07.052 · Cited by 271

Statistical significance of variables driving systematic variation in high-dimensional data

Chung, N. C., & Storey, J. D. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu674 · Cited by 259

Severity and Variability of Depression Symptoms Predicting Suicide Attempt in High-Risk Individuals

Melhem, N., Porta, G., Oquendo, M. A., Zelazny, J. M., Keilp, J. G., Iyengar, S., Burke, A. K., Birmaher, B. B., Stanley, B., Mann, J. J., et al. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.4513 · Cited by 266

A high-level overview of AI ethics

Kazim, E., & Koshiyama, A. S. (2021) · Patterns · doi:10.1016/j.patter.2021.100314 · Cited by 269

Feature selection in bankruptcy prediction

Tsai, C. (2008) · Knowledge-Based Systems · doi:10.1016/j.knosys.2008.08.002 · Cited by 261

Supervised learning with decision tree-based methods in computational and systems biology

Geurts, P., Irrthum, A., & Wehenkel, L. A. (2009) · Molecular BioSystems · doi:10.1039/b907946g · Cited by 259

From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts

Eliashberg, J., Hui, S. K., & Zhang, Z. J. (2007) · Management Science · doi:10.1287/mnsc.1060.0668 · Cited by 249

Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework

Quilty, J., & Adamowski, J. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.05.003 · Cited by 247

Temperature and pH define the realised niche space of arbuscular mycorrhizal fungi

Davison, J., Moora, M., Semchenko, M., Adenan, S., Ahmed, T. A.-F., Ахметжанова, А. А., Alatalo, J. M., Al‐Quraishy, S., Andrianova, E. A., Anslan, S., et al. (2021) · New Phytologist · doi:10.1111/nph.17240 · Cited by 257

Energy variational analysis of ions in water and channels: Field theory for primitive models of complex ionic fluids

Eisenberg, B., Hyon, Y., & Liu, C. (2010) · The Journal of Chemical Physics · doi:10.1063/1.3476262 · Cited by 245

Optimized Data Fusion for Kernel k-Means Clustering

Yu, S. Q., Tranchevent, L.-C., Liu, X., Glänzel, W., Suykens, J. A. K., Moor, B. D., & Moreau, Y. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.255 · Cited by 245

LANDSCAPE ECOLOGY IN NORTH AMERICA: PAST, PRESENT, AND FUTURE

Turner, M. G. (2005) · Ecology · doi:10.1890/04-0890 · Cited by 245

A comparison among interpretative proposals for Random Forests

Aria, M., Cuccurullo, C., & Gnasso, A. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100094 · Cited by 283

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

Jain, A., Hautier, G., Ong, S. P., & Persson, K. (2016) · Journal of Materials Research · doi:10.1557/jmr.2016.80 · Cited by 244

Independent information from cerebrospinal fluid amyloid-β and florbetapir imaging in Alzheimer's disease

Mattsson, N., Insel, P. S., Donohue, M., Landau, S., Jagust, W. J., Shaw, L. M., Trojanowski, J. Q., Zetterberg, H., Blennow, K., & Weiner, M. W. (2015) · Brain · doi:10.1093/brain/awu367 · Cited by 237

Linking data to models: data regression

Jaqaman, K., & Danuser, G. (2006) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2030 · Cited by 243

A survey on predicting the popularity of web content

Tatar, A., de Amorim, M. D., Serge Fdida, B., & Antoniadis, P. (2014) · Journal of Internet Services and Applications · doi:10.1186/s13174-014-0008-y · Cited by 250

Predicting genetic predisposition in humans: the promise of whole-genome markers

Campos, G. D. L., Gianola, D., & Allison, D. B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2898 · Cited by 242

Optimal experimental design and some related control problems

Pronzato, L. (2007) · Automatica · doi:10.1016/j.automatica.2007.05.016 · Cited by 252

Newborn survival: a multi-country analysis of a decade of change

Lawn, J. E., Kinney, M. V., Black, R. E., Pitt, C., Cousens, S. N., Kerber, K. J., Corbett, E., Moran, A. C., Morrissey, C. S., & Oestergaard, M. Z. (2012) · Health Policy and Planning · doi:10.1093/heapol/czs053 · Cited by 243

How is machine learning useful for macroeconomic forecasting?

Coulombe, P. G., Leroux, M., Stevanović, D., & Surprenant, S. (2022) · Journal of Applied Econometrics · doi:10.1002/jae.2910 · Cited by 248

Discovering Mountain Passes via Torchlight: Methods for the Definition of Reaction Coordinates and Pathways in Complex Macromolecular Reactions

Rohrdanz, M. A., Zheng, W., & Clementi, C. (2013) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-040412-110006 · Cited by 242

Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data

Muchlinski, D., Siroky, D., He, J., & Kocher, M. (2016) · Political Analysis · doi:10.1093/pan/mpv024 · Cited by 234

Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ Minimization

Chen, X., Xu, F., & Ye, Y. (2010) · SIAM Journal on Scientific Computing · doi:10.1137/090761471 · Cited by 288

Global Sensitivity Analysis for Systems with Independent and/or Correlated Inputs

Li, G., RABITZ, H. A., Yelvington, P. E., Oluwole, O. O., Bacon, F. W., Kolb, C. E., & Schoendorf, J. (2010) · The Journal of Physical Chemistry A · doi:10.1021/jp9096919 · Cited by 242

The phenotypic and genetic signatures of common musculoskeletal pain conditions

Diatchenko, L., Fillingim, R. B., Smith, S. B., & Maixner, W. (2013) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2013.43 · Cited by 241

Online clustering of parallel data streams

Beringer, J., & Hüllermeier, E. (2005) · Data & Knowledge Engineering · doi:10.1016/j.datak.2005.05.009 · Cited by 241

Improved binary particle swarm optimization using catfish effect for feature selection

Chuang, L., Tsai, S.-W., & Yang, C. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.057 · Cited by 240

Behind the Numbers: Decoding Molecular Phenotypes with Radiogenomics—Guiding Principles and Technical Considerations

Kuo, M. D., & Jamshidi, N. (2014) · Radiology · doi:10.1148/radiol.13132195 · Cited by 243

Stock market one-day ahead movement prediction using disparate data sources

Weng, B., Ahmed, M. A., & Megahed, F. M. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.041 · Cited by 237

Facial feature point detection: A comprehensive survey

Wang, N., Gao, X., Tao, D., Yang, H., & Li, X. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.013 · Cited by 260

Small data in the era of big data

Kitchin, R., & Lauriault, T. P. (2014) · GeoJournal · doi:10.1007/s10708-014-9601-7 · Cited by 255

Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge

Slavakis, K., Giannakis, G. B., & Mateos, G. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2327238 · Cited by 233

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

Ulaszewska, M., Weinert, C. H., Trimigno, A., Portmann, R., Andrés‐Lacueva, C., Badertscher, R., Brennan, L., Brunius, C. F., Bub, A., Capozzi, F., et al. (2018) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201800384 · Cited by 251

Deep learning for drug response prediction in cancer

Baptista, D., Ferreira, P. G., & Rocha, M. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz171 · Cited by 245

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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