The Nature of Statistical Learning Theory

Support vector machine in machine condition monitoring and fault diagnosis

Widodo, A., & Yang, B.-S. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.12.007 · Cited by 1,650

An experimental unification of reservoir computing methods

Verstraeten, D., Schrauwen, B., D’Haene, M., & Stroobandt, D. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.04.003 · Cited by 1,135

Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review

Orrù, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.01.004 · Cited by 1,012

A new learning paradigm: Learning using privileged information

Vapnik, V., & Vashist, A. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.06.042 · Cited by 774

Ensemble of keyword extraction methods and classifiers in text classification

Onan, A., Korukoğlu, S., & Bulut, H. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.03.045 · Cited by 630

Using functional domain composition and support vector machines for prediction of protein subcellular location

Chou, K.-C., & Cai, Y.-D. (2002) · Journal of Biological Chemistry · doi:10.1074/jbc.m204161200 · Cited by 483

New insights into churn prediction in the telecommunication sector: a profit driven data mining approach

Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.09.031 · Cited by 462

Classification of patterns of EEG synchronization for seizure prediction

Mirowski, P., Madhavan, D., LeCun, Y., & Kuzniecky, R. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.09.002 · Cited by 380

Support vector based battery state of charge estimator

Hansen, T., & Wang, C.-J. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.09.020 · Cited by 347

Text-Based Twitter User Geolocation Prediction

Han, B., Cook, P., & Baldwin, T. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4200 · Cited by 324

Ensemble methods for spoken emotion recognition in call-centres

Morrison, D., Wang, R., & De Silva, L. C. (2007) · Speech Communication · doi:10.1016/j.specom.2006.11.004 · Cited by 320

Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images

Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.066 · Cited by 319

Tutorial on practical tips of the most influential data preprocessing algorithms in data mining

García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310

An ensemble system for automatic sleep stage classification using single channel EEG signal

Koley, B., & Dey, D. (2012) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2012.09.012 · Cited by 291

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., de Jong, P. A., Prokop, M., & Ginneken, B. V. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.08.001 · Cited by 289

Support vector machines for predicting membrane protein types by using functional domain composition

Cai, Y.-D., Zhou, G.-P., & Chou, K.-C. (2003) · Biophysical Journal · doi:10.1016/s0006-3495(03)70050-2 · Cited by 284

Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey

Boyacioglu, M. A., Kara, Y., & Baykan, Ö. K. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.003 · Cited by 283

Authentic facial expression analysis

Sebe, N., Lew, M. S., Sun, Y., Cohen, I., Gevers, T., & Huang, T. S. (2007) · Image and Vision Computing · doi:10.1016/j.imavis.2005.12.021 · Cited by 257

Turn-taking cues in task-oriented dialogue

Gravano, A., & Hirschberg, J. (2011) · Computer Speech & Language · doi:10.1016/j.csl.2010.10.003 · Cited by 254

Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews

Thomas, J., McDonald, S., Noel-Storr, A., Shemilt, I., Elliott, J., Mavergames, C., & Marshall, I. J. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.11.003 · Cited by 253

Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

Chen, C., Zhang, Q., Yu, B., Yu, Z., Lawrence, P. J., Ma, Q., & Zhang, Y. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103899 · Cited by 238

Prognostic and diagnostic potential of the structural neuroanatomy of depression

Costafreda, S. G., Chu, C., Ashburner, J., & Fu, C. H. Y. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006353 · Cited by 232

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei, Y., Li, Y., Xu, M., & Huang, W. (2019) · Entropy · doi:10.3390/e21040409 · Cited by 232

A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset

Liu, T., Fan, W., & Wu, C. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101723 · Cited by 220

Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

Erdal, H. I., & Karakurt, O. (2013) · Journal of Hydrology · doi:10.1016/j.jhydrol.2012.11.015 · Cited by 191

Boosting power for clinical trials using classifiers based on multiple biomarkers

Kohannim, O., Hua, X., Hibar, D. P., Lee, S., Chou, Y.-Y., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.022 · Cited by 190

Binary biogeography-based optimization based SVM-RFE for feature selection

Albashish, D., Hammouri, A. I., Braik, M., Atwan, J., & Sahran, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107026 · Cited by 154

A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China

Wang, S., Yu, L., Tang, L., & Wang, S. (2011) · Energy · doi:10.1016/j.energy.2011.09.010 · Cited by 135

Regression-based intensity estimation of facial action units

Savran, A., Sankur, B., & Taha Bilge, M. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2011.11.008 · Cited by 129

Automatic text classification to support systematic reviews in medicine

García Adeva, J. J., Pikatza Atxa, J. M., Ubeda Carrillo, M., & Ansuategi Zengotitabengoa, E. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.08.047 · Cited by 125

Support vector regression for link load prediction

Bermolen, P., & Rossi, D. (2009) · Computer Networks · doi:10.1016/j.comnet.2008.09.018 · Cited by 121

Determination of carbaryl pesticide in Fuji apples using surface-enhanced Raman spectroscopy coupled with multivariate analysis

Fan, Y., Lai, K., Rasco, B. A., & Huang, Y. (2015) · LWT - Food Science and Technology · doi:10.1016/j.lwt.2014.08.011 · Cited by 116

Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images

Wu, W.-J., Lin, S.-W., & Moon, W. K. (2012) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2012.07.004 · Cited by 99

Altered cerebellar–cerebral resting-state functional connectivity reliably identifies major depressive disorder

Ma, Q., Zeng, L.-L., Shen, H., Liu, L., & Hu, D. (2013) · Brain Research · doi:10.1016/j.brainres.2012.12.002 · Cited by 95

Exchange rate prediction using hybrid neural networks and trading indicators

Ni, H., & Yin, H. (2009) · Neurocomputing · doi:10.1016/j.neucom.2008.09.023 · Cited by 94

A reference model for customer-centric data mining with support vector machines

Lessmann, S., & Voß, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2008.12.017 · Cited by 86

Machine learning for microalgae detection and utilization

Ning, H., Li, R., & Zhou, T. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.947394 · Cited by 83

Prediction of the axial compressive strength of circular concrete-filled steel tube columns using sine cosine algorithm-support vector regression

Lyu, F., Fan, X., Ding, F., & Chen, Z. (2021) · Composite Structures · doi:10.1016/j.compstruct.2021.114282 · Cited by 77

Classification of Major Construction Materials in Construction Environments Using Ensemble Classifiers

Son, H., Kim, C., Hwang, N., Kim, C., & Kang, Y. (2014) · Advanced Engineering Informatics · doi:10.1016/j.aei.2013.10.001 · Cited by 74

Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes

Ghosh, A., & Joshi, P. K. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.07.003 · Cited by 69

Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning

Chen, T., Zhang, K., Giannakis, G. B., & Basar, T. (2022) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2021.3078100 · Cited by 67

Identifying scientific artefacts in biomedical literature: The Evidence Based Medicine use case.

Hassanzadeh, H., Groza, T., & Hunter, J. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.02.006 · Cited by 62

Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity

Hu, Y.-H., Tai, C.-T., Liu, K. E., & Cai, C.-F. (2020) · Journal of Informetrics · doi:10.1016/j.joi.2019.101004 · Cited by 62

Fast instance selection for speeding up support vector machines

Chen, J., Zhang, C., Xue, X., & Liu, C.-L. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.01.031 · Cited by 53

Predicting the Regional Adoption of Electric Vehicle (EV) With Comprehensive Models

Jia, J., Shi, B., Che, F., & Zhang, H. (2020) · IEEE Access · doi:10.1109/access.2020.3014851 · Cited by 51

Fast data selection for SVM training using ensemble margin

Guo, L., & Boukir, S. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.08.003 · Cited by 49

A decision support framework to implement optimal personalized marketing interventions

Guelman, L., Guillén, M., & Pérez-Marín, A. M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.01.010 · Cited by 48

Applying machine learning to estimate the optical properties of black carbon fractal aggregates

Luo, J., Zhang, Y., Wang, F., Wang, J., & Zhang, Q. (2018) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2018.05.002 · Cited by 44

Clustering via Hilbert space

Horn, D. (2001) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(01)00442-3 · Cited by 38

Systematic review of machine learning in PTSD studies for automated diagnosis evaluation

Wu, Y., Mao, K., Dennett, L., Zhang, Y., & Chen, J. (2023) · npj Mental Health Research · doi:10.1038/s44184-023-00035-w · Cited by 30

A review of computational intelligence techniques in coral reef-related applications

Salcedo-Sanz, S., Cuadra, L., & Vermeij, M. J. A. (2016) · Ecological Informatics · doi:10.1016/j.ecoinf.2016.01.008 · Cited by 27

Visual-based classification models for grading reclaimed structural timber for reuse: A theoretical, numerical and experimental investigation

Pasca, D. P., Aloisio, A., De Santis, Y., Burkart, H., & Øvrum, A. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2024.119218 · Cited by 15

Deep networks for behavioral variant frontotemporal dementia identification from multiple acquisition sources

Di Benedetto, M., Carrara, F., Tafuri, B., Nigro, S., De Blasi, R., Falchi, F., Gennaro, C., Gigli, G., Logroscino, G., & Amato, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105937 · Cited by 14

The changing fortunes of pattern recognition and computer vision

Chellappa, R. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.04.005 · Cited by 8

P300 Detection Using Ensemble of SVM for Brain-Computer Interface Application

Kundu, S., & Ari, S. (2018) · 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2018.8493903 · Cited by 8

Comparison of Different Classification Methods for Breast Cancer Subtypes Prediction

Xu, J., Wu, P., Chen, Y., & Zhang, L. (2018) · 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · doi:10.1109/spac46244.2018.8965553 · Cited by 5