Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

High-Speed Tracking with Kernelized Correlation Filters

Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753

Hyperspectral remote sensing data analysis and future challenges

Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2244672 · Cited by 2,223

Logistic regression and Artificial Neural Network Classification models: A methodology review

Dreiseitl, S., & Ohno-Machado, L. (2002) · Journal of Biomedical Informatics · doi:10.1016/s1532-0464(03)00034-0 · Cited by 2,177

Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP

Endelman, J. B. (2011) · The Plant Genome · doi:10.3835/plantgenome2011.08.0024 · Cited by 2,094

Quantum Machine Learning in Feature Hilbert Spaces

Schuld, M., & Killoran, N. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.040504 · Cited by 1,840

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477

Fast Human Detection Using a Cascade of Histograms of Oriented Gradients

Zhu, Q., Yeh, M.-C., Cheng, K.-T., & Avidan, S. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.119 · Cited by 1,399

Understanding bag-of-words model: a statistical framework

Zhang, Y., Jin, R., & Zhou, Z.-H. (2010) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-010-0001-0 · Cited by 1,384

Explaining nonlinear classification decisions with deep taylor decomposition

Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357

Object perception as Bayesian inference

Kersten, D., Mamassian, P., & Yuille, A. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142005 · Cited by 1,269

A survey of MRI-based medical image analysis for brain tumor studies

Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/13/r97 · Cited by 1,036

Circuit-centric quantum classifiers

Schuld, M., Bocharov, A., Svore, K. M., & Wiebe, N. (2020) · Physical Review A · doi:10.1103/physreva.101.032308 · Cited by 1,057

Power of data in quantum machine learning

Huang, H.-Y., Broughton, M., Mohseni, M., Babbush, R., Boixo, S., Neven, H., & McClean, J. R. (2021) · Nature Communications · doi:10.1038/s41467-021-22539-9 · Cited by 896

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849

The 2005 PASCAL Visual Object Classes Challenge

Everingham, M., Zisserman, A., Williams, C. K. I., Van Gool, L., Allan, M., Bishop, C. M., Chapelle, O., Dalal, N., Deselaers, T., Dorkó, G., et al. (2006) · Lecture Notes in Computer Science · doi:10.1007/11736790_8 · Cited by 765

Artificial intelligence and machine learning approaches to energy demand-side response: a systematic review

Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651

Gaussian approximation potentials: A brief tutorial introduction

Bartók, A. P., & Csányi, G. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24927 · Cited by 617

A Review of Feature Reduction Techniques in Neuroimaging

Mwangi, B., Tian, T. S., & Soares, J. C. (2014) · Neuroinformatics · doi:10.1007/s12021-013-9204-3 · Cited by 559

Trajectory-Based Anomalous Event Detection

Piciarelli, C., Micheloni, C., & Foresti, G. L. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.2005599 · Cited by 532

An ensemble of fine-tuned convolutional neural networks for medical image classification

Kumar, A., Kim, J., Lyndon, D., Fulham, M., & Feng, D. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2635663 · Cited by 515

Support Vector Machines Used to Estimate the Battery State of Charge

Álvarez Antón, J. C., García Nieto, P. J., Blanco Viejo, C., & Vilán Vilán, J. A. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2013.2243918 · Cited by 512

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

Reinforcement learning and episodic memory in humans and animals: an integrative framework

Gershman, S. J., & Daw, N. D. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033625 · Cited by 446

Riemannian Approaches in Brain-Computer Interfaces: A Review

Yger, F., Berar, M., & Lotte, F. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2627016 · Cited by 420

Learning multiscale and deep representations for classifying remotely sensed imagery

Zhao, W., & Du, S. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.01.004 · Cited by 398

Intelligent prognostics for battery health monitoring based on sample entropy

Widodo, A., Shim, M.-C., Caesarendra, W., & Yang, B.-S. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.03.063 · Cited by 392

KBA: kernel boundary alignment considering imbalanced data distribution

Wu, G., & Chang, E. Y. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.95 · Cited by 380

Potential of quantum computing for drug discovery

Cao, Y., Romero, J., & Aspuru-Guzik, A. (2018) · IBM Journal of Research and Development · doi:10.1147/jrd.2018.2888987 · Cited by 357

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

Advanced BrainAGE in older adults with type 2 diabetes mellitus

Franke, K., Gaser, C., Manor, B., & Novak, V. (2013) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2013.00090 · Cited by 221

Hopfield Networks is All You Need

Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., et al. (2020) · arXiv · doi:10.48550/arxiv.2008.02217 · Cited by 236

Battery state-of-charge estimator using the SVM technique

Álvarez Antón, J. C., García Nieto, P. J., de Cos Juez, F. J., Sánchez Lasheras, F., González Vega, M., & Roqueñí Gutiérrez, M. N. (2013) · Applied Mathematical Modelling · doi:10.1016/j.apm.2013.01.024 · Cited by 211

The successor representation: Its computational logic and neural substrates

Gershman, S. J. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0151-18.2018 · Cited by 191

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

Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks

Ruiz, S., Buyukturkoglu, K., Rana, M., Birbaumer, N., & Sitaram, R. (2014) · Biological Psychology · doi:10.1016/j.biopsycho.2013.04.010 · Cited by 187

Generative embedding for model-based classification of fMRI data

Brodersen, K. H., Schofield, T. M., Leff, A. P., Ong, C. S., Lomakina, E. I., Buhmann, J. M., & Stephan, K. E. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002079 · Cited by 165

Learning to Control in Operational Space

Peters, J., & Schaal, S. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364907087548 · Cited by 162

A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs

Wang, P., Lin, S.-C., & Luo, M. (2016) · 2016 IEEE International Conference on Services Computing (SCC) · doi:10.1109/scc.2016.133 · Cited by 155

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

Robust gait recognition: a comprehensive survey

Rida, I., Almaadeed, N., & Almaadeed, S. (2019) · IET Biometrics · doi:10.1049/iet-bmt.2018.5063 · Cited by 119

Prediction of arrival times of freight traffic on US railroads using support vector regression

Barbour, W., Martinez Mori, J. C., Kuppa, S., & Work, D. B. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.05.019 · Cited by 116

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

Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification

Liu, T., & Abd-Elrahman, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.03.006 · Cited by 103

Mapping patterns of urban development in Ouagadougou, Burkina Faso, using machine learning regression modeling with bi-seasonal Landsat time series

Schug, F., Okujeni, A., Hauer, J., Hostert, P., Nielsen, J. Ø., & van der Linden, S. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.03.022 · Cited by 96

A spline-based tool to assess and visualize the calibration of multiclass risk predictions

Van Hoorde, K., Van Huffel, S., Timmerman, D., Bourne, T., & Van Calster, B. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.12.016 · Cited by 85

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

Prediction of postpartum depression using machine learning techniques from social media text

Fatima, I., Abbasi, B. U. D., Khan, S., Al‐Saeed, M., Ahmad, H. F., & Mumtaz, R. (2019) · Expert Systems · doi:10.1111/exsy.12409 · Cited by 84

Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18 F‐FDG PET/CT

Zhang, Y., Cheng, C., Liu, Z., Wang, L., Pan, G., Sun, G., Chang, Y., Zuo, C., & Yang, X. (2019) · Medical Physics · doi:10.1002/mp.13733 · Cited by 80

Predicting EHL film thickness parameters by machine learning approaches

Marian, M., Mursak, J., Bartz, M., Profito, F. J., Rosenkranz, A., & Wartzack, S. (2023) · Friction · doi:10.1007/s40544-022-0641-6 · Cited by 76

Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning

Vehí, J., Contreras, I., Oviedo, S., Biagi, L., & Bertachi, A. (2020) · Health Informatics Journal · doi:10.1177/1460458219850682 · Cited by 76

MKL for robust multi-modality AD classification

Hinrichs, C., Singh, V., Xu, G., & Johnson, S. (2009) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-04271-3_95 · Cited by 74

Changes of individual BrainAGE during the course of the menstrual cycle

Franke, K., Hagemann, G., Schleussner, E., & Gaser, C. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.036 · Cited by 72

Balancing new against old information: the role of puzzlement surprise in learning

Faraji, M., Preuschoff, K., & Gerstner, W. (2018) · Neural Computation · doi:10.1162/neco_a_01025 · Cited by 68

Improving microalgae growth modeling of outdoor cultivation with light history data using machine learning models: A comparative study

Yeh, Y.-C., Syed, T., Brinitzer, G., Frick, K., Schmid-Staiger, U., Haasdonk, B., Tovar, G. E. M., Krujatz, F., Mädler, J., & Urbas, L. (2023) · Bioresource Technology · doi:10.1016/j.biortech.2023.129882 · Cited by 56

A pixel-based color image segmentation using support vector machine and fuzzy -means

Wang, X.-Y., Zhang, X.-J., Yang, H.-Y., & Bu, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.04.012 · Cited by 42

Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device

Gubbi, J., Kusmakar, S., Rao, A. S., Yan, B., OBrien, T., & Palaniswami, M. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2446539 · Cited by 41

Medical Relation Extraction with Manifold Models

Wang, C., & Fan, J. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1078 · Cited by 40

Automatic classification of literature in systematic reviews on food safety using machine learning

van den Bulk, L. M., Bouzembrak, Y., Gavai, A., Liu, N., van den Heuvel, L. J., & Marvin, H. J. P. (2022) · Current Research in Food Science · doi:10.1016/j.crfs.2021.12.010 · Cited by 38

Model-based feature construction for multivariate decoding

Brodersen, K. H., Haiss, F., Ong, C. S., Jung, F., Tittgemeyer, M., Buhmann, J. M., Weber, B., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.036 · Cited by 36

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

ELM-based spectral–spatial classification of hyperspectral images using extended morphological profiles and composite feature mappings

Argüello, F., & Heras, D. B. (2015) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.999882 · Cited by 27

Speech sound classification and detection of articulation disorders with support vector machines and wavelets

Georgoulas, G., Georgopoulos, V. C., & Stylios, C. D. (2006) · 2006 International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2006.259499 · Cited by 24

Predicting corporate credit risk: Network contagion via trade credit

Berloco, C., De Francisci Morales, G., Frassineti, D., Greco, G., Kumarasinghe, H., Lamieri, M., Massaro, E., Miola, A., & Yang, S. (2021) · PLOS ONE · doi:10.1371/journal.pone.0250115 · Cited by 22

Key-value memory in the brain

Gershman, S. J., Fiete, I., & Irie, K. (2025) · arXiv · doi:10.48550/arxiv.2501.02950 · Cited by 21

Fastfood dictionary learning for periocular-based full face hallucination

Juefei-Xu, F., & Savvides, M. (2016) · 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2016.7791174 · Cited by 19

On Authorship Attribution via Markov Chains and Sequence Kernels

Sanderson, C., & Guenter, S. (2006) · 18th International Conference on Pattern Recognition (ICPR'06) · doi:10.1109/icpr.2006.899 · Cited by 15

A Survey on Data-Driven Methods for State of Charge Estimation of Battery

Satyan, P. A., & Sutar, R. G. (2020) · 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc48915.2020.9155926 · Cited by 12

Quantum computing and supervised machine learning

Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11

Machine Vision Algorithms

Steger, C. (2017) · Handbook of Machine and Computer Vision · doi:10.1002/9783527413409.ch9 · Cited by 6