A tutorial on support vector regression

A Mathematical Theory of Communication

Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918

The Nature of Statistical Learning Theory

Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138

Neural Networks for Pattern Recognition

Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287

The Jackknife, the Bootstrap and Other Resampling Plans

Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120

Robust Statistics

Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066

Spline Models for Observational Data

Wahba, G. (1990) · doi:10.1137/1.9781611970128 · Cited by 4,184

A Probabilistic Theory of Pattern Recognition

Devroye, L., Györfi, L., & Lugosi, G. (1996) · Stochastic Modelling and Applied Probability · doi:10.1007/978-1-4612-0711-5 · Cited by 3,274

On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

Vapnik, V. N., & Chervonenkis, A. Y. (1971) · Theory of Probability & Its Applications · doi:10.1137/1116025 · Cited by 2,448

A Comprehensive Survey on Transfer Learning

Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678

Estimating the Support of a High-Dimensional Distribution

Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168

Support vector machines in remote sensing: a review

Mountrakis, G., Im, J., & Ogole, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.11.001 · Cited by 3,473

Engineering Design via Surrogate Modelling: A Practical Guide

Forrester, A. I. J., Sóbester, A., & Keane, A. J. (2008) · doi:10.1002/9780470770801 · Cited by 2,309

Sparse Representation for Color Image Restoration

Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

Betel, D., Koppal, A., Agius, P., Sander, C., & Leslie, C. (2010) · Genome Biology · doi:10.1186/gb-2010-11-8-r90 · Cited by 1,592

Battery Lifetime Prognostics

Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., et al. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5870 · Cited by 988

Machine learning for internet of things data analysis: a survey

Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.10.002 · Cited by 921

Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages

Zadeh, A., Zellers, R., Pincus, E., & Morency, L.-P. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.94 · Cited by 852

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

Rodríguez-Pérez, R., & Bajorath, J. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00314-0 · Cited by 718

Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods

Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.11.146 · Cited by 664

Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features

Xue, W., Mou, X., Zhang, L., Bovik, A. C., & Feng, X. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2355716 · Cited by 641

On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

Weng, C., Cui, Y., Sun, J., & Peng, H. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.02.012 · Cited by 561

Model learning for robot control: A survey

Nguyen-Tuong, D., & Peters, J. (2011) · Cognitive Processing · doi:10.1007/s10339-011-0404-1 · Cited by 503

Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles

Farmann, A., Waag, W., Marongiu, A., & Sauer, D. U. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.01.129 · Cited by 472

Visualizing high-dimensional data: advances in the past decade

Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., & Pascucci, V. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2640960 · Cited by 427

Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries

Andre, D., Appel, C., Soczka-Guth, T., & Sauer, D. U. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.001 · Cited by 428

A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives

Haynes, J.-D. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.025 · Cited by 402

A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation

Klass, V., Behm, M., & Lindbergh, G. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.116 · Cited by 381

MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos

Zadeh, A., Zellers, R., Pincus, E., & Morency, L.-P. (2016) · arXiv · doi:10.48550/arxiv.1606.06259 · Cited by 350

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis

Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D. B., Paolini, M., Chisholm, K., Kambeitz, J., Haidl, T., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.2165 · Cited by 341

An empirical study of machine learning techniques for affect recognition in human–robot interaction

Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0025-y · Cited by 313

Support vector machines in engineering: an overview

Salcedo‐Sanz, S., Rojo‐Álvarez, J. L., Martínez‐Ramón, M., & Camps‐Valls, G. (2014) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1125 · Cited by 301

Forecasting and operational research: a review

Fildes, R., Nikolopoulos, K., Crone, S. F., & Syntetos, A. A. (2008) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2602597 · Cited by 287

Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

Deberneh, H. M., & Kim, I. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18063317 · Cited by 261

Wheel Defect Detection With Machine Learning

Krummenacher, G., Ong, C. S., Koller, S., Kobayashi, S., & Buhmann, J. M. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2720721 · Cited by 258

Imaging Patterns of Brain Development and their Relationship to Cognition

Erus, G., Battapady, H., Satterthwaite, T. D., Hakonarson, H., Gur, R. E., Davatzikos, C., & Gur, R. C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht425 · Cited by 226

How Intense Are You? Predicting Intensities of Emotions and Sentiments using Stacked Ensemble [Application Notes]

Akhtar, M. S., Ekbal, A., & Cambria, E. (2020) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2019.2954667 · Cited by 246

The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing

Zhu, M., Liu, O. L., & Lee, H.-S. (2020) · Computers & Education · doi:10.1016/j.compedu.2019.103668 · Cited by 229

A novel relational regularization feature selection method for joint regression and classification in ad diagnosis

Zhu, X., Suk, H.-I., Wang, L., Lee, S.-W., & Shen, D. (2017) · Medical Image Analysis · doi:10.1016/j.media.2015.10.008 · Cited by 224

Open issues in genetic programming

O’Neill, M., Vanneschi, L., Gustafson, S., & Banzhaf, W. (2010) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-010-9113-2 · Cited by 224

Studying user income through language, behaviour and affect in social media

Preoţiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., & Aletras, N. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138717 · Cited by 219

The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs

Biel, J.-I., & Gatica-Perez, D. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2225032 · Cited by 209

Online state of health estimation on NMC cells based on predictive analytics

Berecibar, M., Devriendt, F., Dubarry, M., Villarreal, I., Omar, N., Verbeke, W., & Van Mierlo, J. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.04.109 · Cited by 202

A novel matrix-similarity based loss function for joint regression and classification in ad diagnosis

Zhu, X., Suk, H.-I., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.078 · Cited by 202

A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries

Zhao, Q., Qin, X., Zhao, H., & Feng, W. (2018) · Microelectronics Reliability · doi:10.1016/j.microrel.2018.04.007 · Cited by 199

Bridging the divide in financial market forecasting: machine learners vs. financial economists

Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., & Johnson, J. E. V. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.05.033 · Cited by 195

Capillary electrophoresis–mass spectrometry as a powerful tool in biomarker discovery and clinical diagnosis: An update of recent developments

Mischak, H., Coon, J. J., Novak, J., Weissinger, E. M., Schanstra, J. P., & Dominiczak, A. F. (2009) · Mass Spectrometry Reviews · doi:10.1002/mas.20205 · Cited by 193

Predicting mild cognitive impairment from spontaneous spoken utterances

Asgari, M., Kaye, J., & Dodge, H. (2017) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1016/j.trci.2017.01.006 · Cited by 185

Machine learning for next-generation intelligent transportation systems: A survey

Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4427 · Cited by 179

Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets

van Westen, G. J. P., Wegner, J. K., IJzerman, A. P., van Vlijmen, H. W. T., & Bender, A. (2011) · MedChemComm · doi:10.1039/c0md00165a · Cited by 176

Reaching the end-game for GWAS: Machine learning approaches for the prioritization of complex disease loci

Nicholls, H. L., John, C. R., Watson, D. S., Munroe, P. B., Barnes, M. R., & Cabrera, C. P. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00350 · Cited by 155

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets

Marchese Robinson, R. L., Palczewska, A., Palczewski, J., & Kidley, N. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00753 · Cited by 167

Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

Kessler, R. C., Stein, M. B., Petukhova, M. V., Bliese, P., Bossarte, R. M., Bromet, E. J., Fullerton, C. S., Gilman, S. E., Ivany, C., Lewandowski-Romps, L., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2016.110 · Cited by 163

PEAKS – A system for the automatic evaluation of voice and speech disorders

Maier, A., Haderlein, T., Eysholdt, U., Rosanowski, F., Batliner, A., Schuster, M., & Nöth, E. (2009) · Speech Communication · doi:10.1016/j.specom.2009.01.004 · Cited by 163

Machine learning applied to road safety modeling: A systematic literature review

Silva, P. B., Andrade, M., & Ferreira, S. (2020) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2020.07.004 · Cited by 162

Prediction of brain maturity in infants using machine-learning algorithms

Smyser, C. D., Dosenbach, N. U. F., Smyser, T. A., Snyder, A. Z., Rogers, C. E., Inder, T. E., Schlaggar, B. L., & Neil, J. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.029 · Cited by 140

Multimodal Multi-task Learning for Dimensional and Continuous Emotion Recognition

Chen, S., Jin, Q., Zhao, J., & Wang, S. (2017) · Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge · doi:10.1145/3133944.3133949 · Cited by 147

City Forensics: Using Visual Elements to Predict Non-Visual City Attributes

Arietta, S. M., Efros, A. A., Ramamoorthi, R., & Agrawala, M. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346446 · Cited by 139

Predictive Analytics for Complex IoT Data Streams

Akbar, A., Khan, A., Carrez, F., & Moessner, K. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2712672 · Cited by 136

A Machine Learning Approach to TCP Throughput Prediction

Mirza, M., Sommers, J., Barford, P., & Zhu, X. (2010) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2009.2037812 · Cited by 132

Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

Tang, X., Yao, H., Sun, Y., Aggarwal, C., Mitra, P., & Wang, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6056 · Cited by 125

Deep Learning for Forecasting Stock Returns in the Cross-Section

Abe, M., & Nakayama, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93034-3_22 · Cited by 124

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

Chemistry-informed machine learning prediction of compressive strength for alkali-activated materials

Zhang, L. V., Marani, A., & Nehdi, M. L. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.126103 · Cited by 119

Trajectory planning for optimal robot catching in real-time

Lampariello, R., Nguyen-Tuong, D., Castellini, C., Hirzinger, G., & Peters, J. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980114 · Cited by 117

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

Decoding post-stroke motor function from structural brain imaging

Rondina, J. M., Filippone, M., Girolami, M., & Ward, N. S. (2016) · NeuroImage: Clinical · doi:10.1016/j.nicl.2016.07.014 · Cited by 109

Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington's disease using machine learning techniques

Rizk-Jackson, A., Stoffers, D., Sheldon, S., Kuperman, J., Dale, A., Goldstein, J., Corey-Bloom, J., Poldrack, R. A., & Aron, A. R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.273 · Cited by 107

Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00267-x · Cited by 112

Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells

Zhao, Z.-W., del Cueto, M., Geng, Y., & Troisi, A. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c02325 · Cited by 102

Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory

Kruppa, J., Liu, Y., Biau, G., Kohler, M., König, I. R., Malley, J. D., & Ziegler, A. (2014) · Biometrical Journal · doi:10.1002/bimj.201300068 · Cited by 97

Machine learning-enabled optimization of extrusion-based 3D printing

Rahmani Dabbagh, S., Ozcan, O., & Tasoglu, S. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.002 · Cited by 94

Expert System for Predicting Reaction Conditions: The Michael Reaction Case

Marcou, G., Aires de Sousa, J., Latino, D. A. R. S., de Luca, A., Horvath, D., Rietsch, V., & Varnek, A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500698a · Cited by 86

Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients

Varnek, A., Gaudin, C., Marcou, G., Baskin, I., Pandey, A. K., & Tetko, I. V. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8002914 · Cited by 86

A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine

Zhong, W., Zhuang, Y., Sun, J., & Gu, J. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1194-2 · Cited by 83

On User-Centric Modular QoE Prediction for VoIP Based on Machine-Learning Algorithms

Charonyktakis, P., Plakia, M., Tsamardinos, I., & Papadopouli, M. (2016) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2015.2461216 · Cited by 75

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

Estimating residential energy consumption in metropolitan areas: A microsimulation approach

Zhang, W., Robinson, C., Guhathakurta, S., Garikapati, V. M., Dilkina, B., Brown, M. A., & Pendyala, R. M. (2018) · Energy · doi:10.1016/j.energy.2018.04.161 · Cited by 68

Disaggregating asthma: big investigation versus big data

Belgrave, D., Henderson, J., Simpson, A., Buchan, I., Bishop, C., & Custovic, A. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2016.11.003 · Cited by 67

Machine learning‐based multimodal prediction of language outcomes in chronic aphasia

Kristinsson, S., Zhang, W., Rorden, C., Newman‐Norlund, R., Basilakos, A., Bonilha, L., Yourganov, G., Xiao, F., Hillis, A., & Fridriksson, J. (2021) · Human Brain Mapping · doi:10.1002/hbm.25321 · Cited by 64

Machine learning in materials design: Algorithm and application*

Song, Z., Chen, X., Meng, F., Cheng, G., Wang, C., Sun, Z., & Yin, W.-J. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abc0e3 · Cited by 63

Using Data Mining for Wine Quality Assessment

Cortez, P., Teixeira, J., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-04747-3_8 · Cited by 60

Determining the Veracity of Rumours on Twitter

Giasemidis, G., Singleton, C., Agrafiotis, I., Nurse, J. R. C., Pilgrim, A., Willis, C., & Greetham, D. V. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-47880-7_12 · Cited by 60

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

Pruett, J. R., Kandala, S., Hoertel, S., Snyder, A. Z., Elison, J. T., Nishino, T., Feczko, E., Dosenbach, N. U. F., Nardos, B., Power, J. D., et al. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2015.01.003 · Cited by 57

Analyzing high speed rail passengers’ train choices based on new online booking data in China

Sun, Y., Jiang, Z., Gu, J., Zhou, M., Li, Y., & Zhang, L. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.10.015 · Cited by 50

Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images

Lai, L., Zhang, Y., Cao, Z., Liu, Z., & Yang, Q. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2023.163357 · Cited by 43

Multimodal deep learning methods enhance genomic prediction of wheat breeding

Montesinos-López, A., Rivera, C., Pinto, F., Piñera, F., Gonzalez, D., Reynolds, M., Pérez-Rodríguez, P., Li, H., Montesinos-López, O. A., & Crossa, J. (2023) · G3: Genes, Genomes, Genetics · doi:10.1093/g3journal/jkad045 · Cited by 41

Comparison of machine learning methods and finite element analysis on the fracture behavior of polymer composites

Balcıoğlu, H. E., & Seçkin, A. Ç. (2021) · Archive of Applied Mechanics · doi:10.1007/s00419-020-01765-5 · Cited by 40

Battery SOH estimation based on decision tree and improved support vector machine regression algorithm

Qian, L., Xuan, L., & Chen, J. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1218580 · Cited by 38

QoE-driven in-network optimization for Adaptive Video Streaming based on packet sampling measurements

Bouten, N., Schmidt, R. D. O., Famaey, J., Latré, S., Pras, A., & De Turck, F. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.02.007 · Cited by 36

An Equivalence between the Lasso and Support Vector Machines

Jaggi, M. (2013) · arXiv · doi:10.48550/arxiv.1303.1152 · Cited by 35

Evaluating Real-Life Performance of Lithium-Ion Battery Packs in Electric Vehicles

Klass, V., Behm, M., & Lindbergh, G. (2012) · Journal of The Electrochemical Society · doi:10.1149/2.047211jes · Cited by 33

A recommender system for predicting students' admission to a graduate program using machine learning algorithms

El Guabassi, I., Bousalem, Z., Marah, R., & Qazdar, A. (2021) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v17i02.20049 · Cited by 32

Bio-signal based elbow angle and torque simultaneous prediction during isokinetic contraction

Zhou, Y., Liu, J., Zeng, J., Li, K., & Liu, H. (2019) · Science China Technological Sciences · doi:10.1007/s11431-018-9354-5 · Cited by 30

A Nonlinear Flux Linkage Model for Bearingless Induction Motor Based on GWO-LSSVM

Li, K., Cheng, G., Sun, X., & Yang, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2905247 · Cited by 30

Reducing Tropical Cyclone Prediction Errors Using Machine Learning Approaches

Richman, M. B., Leslie, L. M., Ramsay, H. A., & Klotzbach, P. J. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.09.048 · Cited by 30

Machine Learning for Film Thickness Prediction in Elastohydrodynamic Lubricated Elliptical Contacts

Issa, J., El Hajj, A., Vergne, P., & Habchi, W. (2023) · Lubricants · doi:10.3390/lubricants11120497 · Cited by 30

Machine learning guided design of single-molecule magnets for magnetocaloric applications

Holleis, L., Shivaram, B. S., & Balachandran, P. V. (2019) · Applied Physics Letters · doi:10.1063/1.5094553 · Cited by 29

Smart Experts for Network State Estimation

Edalat, Y., Ahn, J.-S., & Obraczka, K. (2016) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2016.2586506 · Cited by 27

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

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants

Nitika, N., Keerthiveena, B., Thakur, G., & Rathore, A. S. (2024) · Pharmaceutical Research · doi:10.1007/s11095-024-03663-9 · Cited by 26

Genomic Selection for Small Grain Improvement

Rutkoski, J. E., Crain, J., Poland, J., & Sorrells, M. E. (2017) · Genomic Selection for Crop Improvement · doi:10.1007/978-3-319-63170-7_5 · Cited by 24

Damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression

Lu, S., Jiang, M., Wang, X., Yu, H., & Su, C. (2019) · Optik · doi:10.1016/j.ijleo.2018.11.086 · Cited by 15

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

Putcha, A., Nguyen, T., Smith, R., Choffin, R., & Bai, W. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200351 · Cited by 10

Determining the Concentration of Polycyclic Aromatic Hydrocarbons in Water Using Surface Enhanced Raman Spectroscopy and Kernel Principal Components Analysis Combined with Support Vector Regression

Jian, C., Boyan, J., Ying, Z., & Zhenyu, W. (2021) · Journal of Applied Spectroscopy · doi:10.1007/s10812-021-01161-z · Cited by 10

Use of Support Vector Machines to Assign Short-Term Counts to Seasonal Adjustment Factor Groups

Tsapakis, I., & Schneider, W. H. (2015) · Transportation Research Record: Journal of the Transportation Research Board · doi:10.3141/2527-02 · Cited by 10

An artificial neural network model to predict structure-based protein–protein free energy of binding from Rosetta-calculated properties

Ferraz, M. V. F., Neto, J. C. S., Lins, R. D., & Teixeira, E. S. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05644e · Cited by 9

Aphasia severity prediction using a multi-modal machine learning approach

Hu, X., Varkanitsa, M., Kropp, E., Betke, M., Ishwar, P., & Kiran, S. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121300 · Cited by 8