LIBSVM: A library for support vector machines

Support-vector networks

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

Statistical Learning Theory

Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236

A training algorithm for optimal margin classifiers

Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992

A comparison of methods for multiclass support vector machines

Hsu, C., & Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,923

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

Fast Training of Support Vector Machines Using Sequential Minimal Optimization

Platt, J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,501

A Practical Guide to Support Vector Classication

Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,056

Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods

Platt, J. C. (1999) · Cited by 4,834

Making Large-Scale SVM Learning PracticalPreprint

Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,517

Untitled work

(1999) · doi:10.1162/089976600300015565 · Cited by 2,799

A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

Li, Y., Xu, M., Yu, W., & Huang, W. (2015) · Measurement · doi:10.1016/j.measurement.2015.08.034 · Cited by 276

Segmentation and Classification of Hyperspectral Images Using Minimum Spanning Forest Grown From Automatically Selected Markers

Tarabalka, Y., Chanussot, J., & Benediktsson, J. A. (2010) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2037132 · Cited by 275

Prediction of energy consumption in hotel buildings via support vector machines

Shao, M., Wang, X., Bu, Z., Chen, X., & Wang, Y. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102128 · Cited by 275

Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer

Durbha, S. S., King, R. L., & Younan, N. H. (2007) · Remote Sensing of Environment · doi:10.1016/j.rse.2006.09.031 · Cited by 282

Sentiment, emotion, purpose, and style in electoral tweets

Mohammad, S. M., Zhu, X., Kiritchenko, S., & Martin, J. (2014) · Information Processing & Management · doi:10.1016/j.ipm.2014.09.003 · Cited by 274

Multiple Kernel Learning for Visual Object Recognition: A Review

Bucak, S. S., Jin, R., & Jain, A. K. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.212 · Cited by 274

Speech emotion recognition based on HMM and SVM

Lin, Y., & Wei, G. (2005) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2005.1527805 · Cited by 274

What helps where – and why? Semantic relatedness for knowledge transfer

Rohrbach, M., Stark, M. M., Szarvas, G., Gurevych, I., & Schiele, B. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540121 · Cited by 273

SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

Zhang, J., Lin, X., & Ning, X. (2013) · Remote Sensing · doi:10.3390/rs5083749 · Cited by 273

Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment

Kollreider, K., Fronthaler, H., Faraj, M. I., & Bigün, J. (2007) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2007.902037 · Cited by 273

Turning from TF-IDF to TF-IGM for term weighting in text classification

Chen, K., Zhang, Z., Long, J., & Zhang, H. (2016) · Expert systems with applications · doi:10.1016/j.eswa.2016.09.009 · Cited by 266

No-Reference Image Quality Assessment Using Visual Codebooks

Ye, P., & Doermann, D. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2190086 · Cited by 272

Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods

Lin, S.-S., Shen, S., Zhou, A., & Xu, Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103490 · Cited by 279

AVEC 2011–The First International Audio/Visual Emotion Challenge

Schuller, B. W., Valstar, M., Eyben, F., McKeown, G. J., Cowie, R., & Pantić, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_53 · Cited by 270

Objective Quality Evaluation of Dehazed Images

Min, X., Zhai, G., Gu, K., Yang, X., & Guan, X. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2868771 · Cited by 275

High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Akusok, A., Björk, K.-M., Miché, Y., & Lendasse, A. (2015) · IEEE Access · doi:10.1109/access.2015.2450498 · Cited by 276

Functional subregions of the human entorhinal cortex

Maaß, A., Berron, D., Libby, L. A., Ranganath, C., & Düzel, E. (2015) · eLife · doi:10.7554/elife.06426 · Cited by 266

Strong Evidence for Pattern Separation in Human Dentate Gyrus

Berron, D., Schütze, H., Maass, A., Cárdenas‐Blanco, A., Kuijf, H. J., Kumaran, D., & Düzel, E. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0518-16.2016 · Cited by 299

Effective feature learning and fusion of multimodality data using stage‐wise deep neural network for dementia diagnosis

Zhou, T., Thung, K., Zhu, X., & Shen, D. G. (2018) · Human Brain Mapping · doi:10.1002/hbm.24428 · Cited by 276

An Empirical Study of Fault Localization Families and Their Combinations

Zou, D., Liang, J., Xiong, Y., Ernst, M. D., & Zhang, L. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2892102 · Cited by 282

BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models

Li, H., Pang, Y., & Liu, B. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab829 · Cited by 275

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

Zhang, L., Zhang, Q., Du, B., Huang, X., Tang, Y. Y., & Tao, D. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2605044 · Cited by 268

Chromatin profiles classify castration-resistant prostate cancers suggesting therapeutic targets

Tang, F., Xu, D., Wang, S., Wong, C. K., Martinez‐Fundichely, A., Lee, C. J., Cohen, S. M., Park, J., Hill, C. E., Eng, K. W., et al. (2022) · Science · doi:10.1126/science.abe1505 · Cited by 272

Use of a dark object concept and support vector machines to automate forest cover change analysis

Huang, C., Song, K., Kim, S., TOWNSHEND, J. R. G., Davis, P. W., Masek, J. G., & Goward, S. N. (2007) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.07.023 · Cited by 268

Food recognition using statistics of pairwise local features

Yang, S., Chen, M., Pomerleau, D., & Sukthankar, R. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539907 · Cited by 268

No-Reference Quality Assessment of Natural Stereopairs

Chen, M.-J., Cormack, L. K., & Bovik, A. C. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2267393 · Cited by 268

Support Vector Machine for Multifrequency SAR Polarimetric Data Classification

Lardeux, C., Frison, P., Tison, C., Souyris, J., Stoll, B., Fruneau, B., & Rudant, J. P. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2023908 · Cited by 274

Reduced Support Vector Machines: A Statistical Theory

Lee, Y., & Huang, S. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.883722 · Cited by 268

A Classification Framework for Anomaly Detection

Steinwart, I., Hush, D. R., & Scovel, C. (2005) · Cited by 268

Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data

Redlich, R., Opel, N., Grotegerd, D., Dohm, K., Zaremba, D., Bürger, C., Münker, S., Mühlmann, L., Wahl, P., Heindel, W. L., et al. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.0316 · Cited by 271

Learning Everything about Anything: Webly-Supervised Visual Concept Learning

Divvala, S. K., Farhadi, A., & Guestrin, C. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.412 · Cited by 267

New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes

Zhao, Y.-Q., Zeng, D., Laber, E. B., & Kosorok, M. R. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.937488 · Cited by 273

In silico Prediction of Chemical Ames Mutagenicity

Xu, C., Cheng, F., Chen, L., Du, Z., Li, W., Liu, G., Lee, P. W., & Tang, Y. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300400a · Cited by 273

Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing

Mayank Vatsa, A., Singh, R., & Afzel Noore, B. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.922059 · Cited by 267

Speech emotion recognition based on feature selection and extreme learning machine decision tree

Liu, Z., Wu, M., Cao, W., Mao, J.-W., Xu, J., & Tan, G. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.07.050 · Cited by 267

A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem

Dong, Y., Li, Y., Zheng, H., Wang, R., & Xu, M. (2021) · ISA Transactions · doi:10.1016/j.isatra.2021.03.042 · Cited by 272

From Paraphrase Database to Compositional Paraphrase Model and Back

Wieting, J., Bansal, M., Gimpel, K., & Livescu, K. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00143 · Cited by 267

Recent Advances of Large-Scale Linear Classification

Yuan, G.-X., Ho, C.-H., & Lin, C. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2188013 · Cited by 280

Spectral–Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model

Fang, L., Shutao Li, A., Kang, X., & Benediktsson, J. A. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2392755 · Cited by 266

Analysis of Learning from Positive and Unlabeled Data

du Plessis, M. C., Niu, G., & Sugiyama, M. (2014) · neural information processing systems · Cited by 266

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Cao, J., Leng, W., Liu, K., Liu, L., He, Z., & Zhu, Y. (2018) · Remote Sensing · doi:10.3390/rs10010089 · Cited by 266

Robust Sound Event Classification Using Deep Neural Networks

McLoughlin, I. V., Zhang, H. S., Xie, Z., Song, Y., & Wei, X. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2389618 · Cited by 265

Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2009) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2009/783194 · Cited by 265

Chemotaxonomic Identification of Single Bacteria by Micro-Raman Spectroscopy: Application to Clean-Room-Relevant Biological Contaminations

Rösch, P., Harz, M., Schmitt, M. P., Peschke, K., Ronneberger, O., Burkhardt, H., Motzkus, H., Lankers, M., Hofer, S., Thiele, H., et al. (2005) · Applied and Environmental Microbiology · doi:10.1128/aem.71.3.1626-1637.2005 · Cited by 265

Development of a widely targeted volatilomics method for profiling volatilomes in plants

Yuan, H., Cao, G., Hou, X., Huang, M., Du, P., Tan, T., Zhang, Y., Zhou, H., Liu, X., Liu, L., et al. (2021) · Molecular Plant · doi:10.1016/j.molp.2021.09.003 · Cited by 271

Explaining Data-Driven Document Classifications1

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

Combining content and link for classification using matrix factorization

Shenghuo Zhu, B., Yu, K., Chi, Y., & Gong, Y. (2007) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1277741.1277825 · Cited by 266

Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning

Rubinstein, B. I. P., Bartlett, P. L., Huang, L., & Taft, N. (2012) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.v4i1.612 · Cited by 298

Learning Features From Music Audio With Deep Belief Networks.

Hamel, P., & Eck, D. (2010) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1414969 · Cited by 264

Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system

Pławiak, P. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.022 · Cited by 272

Assessing the Quality of Actions

Pirsiavash, H., Vondrick, C., & Torralba, A. B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_36 · Cited by 263

Cancer diagnosis with DNA molecular computation

Zhang, C., Zhao, Y., Xu, X., Xu, R., Li, H., Teng, X., Du, Y., Miao, Y., Lin, H.-C., & Han, D. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0699-0 · Cited by 264

Epileptic seizure prediction using relative spectral power features

Bandarabadi, M., Teixeira, C., Rasekhi, J., & Dourado, A. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.05.022 · Cited by 264

SUN: Top-down saliency using natural statistics

Kanan, C., Tong, M. H., Zhang, L., & Cottrell, G. W. (2009) · Visual Cognition · doi:10.1080/13506280902771138 · Cited by 270

Comparing Prediction Performance for Crash Injury Severity Among Various Machine Learning and Statistical Methods

Zhang, J., Li, Z., Pu, Z., & Xu, C. (2018) · IEEE Access · doi:10.1109/access.2018.2874979 · Cited by 280

Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews

Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. K. (2019) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2019.01.003 · Cited by 262

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Hodgdon, T., McInnes, M. D. F., Schieda, N., Flood, T. A., Lamb, L. R., & Thornhill, R. E. (2015) · Radiology · doi:10.1148/radiol.2015142215 · Cited by 261

Nonparallel Support Vector Machines for Pattern Classification

Tian, Y., Qi, Z., Ju, X., Shi, Y. C., & Liu, X. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2279167 · Cited by 259

Integration of image quality and motion cues for face anti-spoofing: A neural network approach

Feng, L., Po, L.-M., Li, Y., Xu, X., Fang, Y., Cheung, T. C.-H., & Cheung, K.-W. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.03.019 · Cited by 261

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Xu, Y., Chen, H., Heidari, A. A., Luo, J., Zhang, Q., Zhao, X., & Li, C. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.03.043 · Cited by 263

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Karpov, P., Godin, G., & Tetko, I. V. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00423-w · Cited by 275

Query-driven active surveying for collective classification

Namata, G. M. S., London, B., Getoor, L. C., & Huang, B. (2012) · Mining and Learning with Graphs · Cited by 276

Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning

Gao, X., Lin, S., & Wong, T. Y. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2444389 · Cited by 260

Vision-Based Material Recognition for Automated Monitoring of Construction Progress and Generating Building Information Modeling from Unordered Site Image Collections

Dimitrov, A., & Golparvar-Fard, M. (2014) · Advanced Engineering Informatics · doi:10.1016/j.aei.2013.11.002 · Cited by 261

Age estimation using a hierarchical classifier based on global and local facial features

Choi, S., Lee, Y. J., Lee, S. J., Park, K. R., & Kim, J. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.12.005 · Cited by 260

Training structural SVMs when exact inference is intractable

Finley, T., & Joachims, T. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390195 · Cited by 260

Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study

Jiang, Y., Yang, J. J., Ngo, C., & Hauptmann, A. G. (2009) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2009.2036235 · Cited by 257

Fortune Teller: Predicting Your Career Path

Liu, Y., Zhang, L., Nie, L., Yan, Y., & Rosenblum, D. S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9969 · Cited by 260

Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach

Li, X., & Chen, H. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.09.019 · Cited by 259

A comparative study of different machine learning methods on microarray gene expression data

Pirooznia, M., Yang, J. Y., Yang, M. Q., & Deng, Y. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-s1-s13 · Cited by 262

Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set

Sushko, I., Novotarskyi, S., Körner, R., Pandey, A. K., Cherkasov, A. R., Li, J., Gramatica, P., Hansen, K., Schroeter, T. S., Müller, K., et al. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100253r · Cited by 269

Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder

Ma, X., Wang, H., & Geng, J. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2517204 · Cited by 260

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Goudjil, M., Koudil, M., Bedda, M., & Ghoggali, N. (2016) · Machine Intelligence Research · doi:10.1007/s11633-015-0912-z · Cited by 259

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Junior, J. D. S. F., Carvalho, E., Ferreira, B. V., de Souza, C. R. B., Suhara, Y., Pentland, A., & Pessin, G. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174959 · Cited by 259

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Peng, K., Chen, T., Sadovnik, A., & Gallagher, A. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298687 · Cited by 268

Learning a blind measure of perceptual image quality

Tang, H., Joshi, N., & Kapoor, A. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995446 · Cited by 260

An Integrated Text Analytic Framework for Product Defect Discovery

Abrahams, A. S., Fan, W., Wang, G. A., Zhang, Z., & Jiao, J. (2014) · Production and Operations Management · doi:10.1111/poms.12303 · Cited by 258

Cross-correlation aided support vector machine classifier for classification of EEG signals

Chandaka, S., Chatterjee, A., & Munshi, S. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.11.017 · Cited by 258

Single-molecule spectroscopy of amino acids and peptides by recognition tunnelling

Zhao, Y., Ashcroft, B. A., Zhang, P., Liu, H., Sen, S., Song, W., Im, J., Gyarfas, B., Manna, S. K., Biswas, S., et al. (2014) · Nature Nanotechnology · doi:10.1038/nnano.2014.54 · Cited by 258

Virtual Screening of Molecular Databases Using a Support Vector Machine

Jorissen, R. N., & Gilson, M. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049641u · Cited by 259

DeepFish: Accurate underwater live fish recognition with a deep architecture

Qin, H., Li, X., Liang, J., Peng, Y., & Zhang, C. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.10.122 · Cited by 265

A Convex Formulation for Semi-Supervised Multi-Label Feature Selection

Chang, X., Nie, F., Yang, Y., & Huang, H. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8922 · Cited by 258

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion

Byra, M., Galperin, M., Ojeda‐Fournier, H., Olson, L., O'Boyle, M., Comstock, C., & Andre, M. (2019) · Medical Physics · doi:10.1002/mp.13361 · Cited by 253

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Cieslak, D. A., & Chawla, N. V. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87479-9_34 · Cited by 264

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Qi, Z., Yeh, A. G. O., Li, X., & Zheng, L. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.11.001 · Cited by 257

A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting

Huang, C.-L., & Tsai, C.-Y. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.11.062 · Cited by 257

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Zhai, Y., Ong, Y.-S., & Tsang, I. W.-H. (2014) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2014.2326099 · Cited by 251

Large-Margin Multi-ViewInformation Bottleneck

Xu, C., Tao, D., & Xu, C. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.2296528 · Cited by 255

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

Khokhar, Z. O., Xiao, Z. G., & Menon, C. (2010) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-9-41 · Cited by 257

Multi-aspect Sentiment Analysis with Topic Models

Lu, B., Ott, M., Cardie, C., & Tsou, B. K. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.125 · Cited by 262

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Liu, Z., Xiao, X., Yu, D., Jia, J., Qiu, W., & Chou, K. (2015) · Analytical Biochemistry · doi:10.1016/j.ab.2015.12.017 · Cited by 257

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Battineni, G., Chintalapudi, N., & Amenta, F. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.100200 · Cited by 280

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Jayasinghe, U., Lee, G. M., Um, T.-W., & Shi, Q. (2019) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2018.2839623 · Cited by 256

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

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Wang, F., Zhong, S.-H., Peng, J., Jiang, J., & Liu, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-73600-6_8 · Cited by 256

Land cover mapping of large areas using chain classification of neighboring Landsat satellite images

Knorn, J., Rabe, A., Radeloff, V. C., Kuemmerle, T., Kozak, J., & Hostert, P. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.01.010 · Cited by 263

Occupancy Detection from Electricity Consumption Data

Kleiminger, W., Beckel, C., Staake, T., & Silvia Santini, B. (2013) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/2528282.2528295 · Cited by 256

MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data

Zhou, X., & Tuck, D. P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm036 · Cited by 258

A review of optimization methodologies in support vector machines

Shawe-Taylor, J. S., & Sun, S. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.06.026 · Cited by 256

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