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 · see citing works

Statistical Learning Theory

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

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 · see citing works

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 6,861 · see citing works

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 · see citing works

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 · see citing works

A Practical Guide to Support Vector Classication

Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,070 · see citing works

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

Platt, J. C. (1999) · Cited by 4,834 · see citing works

Making Large-Scale SVM Learning PracticalPreprint

Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,514 · see citing works

Untitled work

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

Training support vector machines: an application to face detection

Osuna, E., Freund, R. M., & Girosi, F. (2002) · 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.1997.609310 · Cited by 2,481 · see citing works

Improvements to Platt's SMO Algorithm for SVM Classifier Design

Keerthi, S. S., Shevade, S., Bhattacharyya, C., & Murthy, K. R. K. (2001) · Neural Computation · doi:10.1162/089976601300014493 · Cited by 1,791 · see citing works

Probability Estimates for Multi-class Classification by Pairwise Coupling

Wu, T., Lin, C., & Weng, R. C. (2004) · Journal of Machine Learning Research · doi:10.5555/1005332.1016791 · Cited by 1,673 · see citing works

Large Margin DAG's for Multiclass Classification

Platt, J. C., Cristianini, N., & Shawe-Taylor, J. S. (1999) · Cited by 1,637 · see citing works

The pyramid match kernel: discriminative classification with sets of image features

Grauman, K. L., & Darrell, T. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.239 · Cited by 1,500 · see citing works

Working Set Selection Using Second Order Information for Training Support Vector Machines

Fan, R.-E., Chen, P.-H., & Lin, C. (2005) · Journal of Machine Learning Research · Cited by 1,409 · see citing works

Interpolation of Scattered Data: Distance Matrices and Conditionally Positive Definite Functions

Micchelli, C. A. (1984) · Springer eBooks · doi:10.1007/978-94-009-6466-2_7 · Cited by 1,308 · see citing works

Multiclass Support Vector Machines

(2005) · Springer eBooks · doi:10.1007/1-84628-219-5_3 · Cited by 1,066 · see citing works

Pairwise Classification and Support Vector Machines

Kreßel, U. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0020 · Cited by 1,021 · see citing works

A note on Platt’s probabilistic outputs for support vector machines

Lin, H.-T., Lin, C., & Weng, R. C. (2007) · Machine Learning · doi:10.1007/s10994-007-5018-6 · Cited by 876 · see citing works

Single-layer learning revisited: a stepwise procedure for building and training a neural network

Knerr, S., Personnaz, L., & Dreyfus, G. (1990) · Springer eBooks · doi:10.1007/978-3-642-76153-9_5 · Cited by 837 · see citing works

MaltParser: A language-independent system for data-driven dependency parsing

Nivre, J., Hall, J., Nilsson, J., Chanev, A., Eryiğit, G., Kübler, S., Marinov, S., & Marsi, E. (2007) · Natural Language Engineering · doi:10.1017/s1351324906004505 · Cited by 801 · see citing works

Support Vector Machines: Training and Applications

Osuna, E., Freund, R. M., & Girosi, F. (1997) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 701 · see citing works

RSVM: Reduced Support Vector Machines

Lee, Y.-J., & Mangasarian, O. L. (2001) · Proceedings of the 2001 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972719.13 · Cited by 647 · see citing works

A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods

Lin, H.-T. (2005) · Cited by 589 · see citing works

Efficient svm training using low-rank kernel representations

Fine, S., & Scheinberg, K. (2002) · Cited by 584 · see citing works

Another approach to polychotomous classification

Friedman, J. H. (1996) · Medical Entomology and Zoology · Cited by 582 · see citing works

Training v-Support Vector Classifiers: Theory and Algorithms

Chang, C.-C., & Lin, C. (2001) · Neural Computation · doi:10.1162/089976601750399335 · Cited by 502 · see citing works

A tutorial on ν ‐support vector machines

Chen, P.-H., Lin, C., & Schölkopf, B. (2005) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.537 · Cited by 481 · see citing works

PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V., & Pollmann, S. (2009) · Neuroinformatics · doi:10.1007/s12021-008-9041-y · Cited by 468 · see citing works

A Study on SMO-Type Decomposition Methods for Support Vector Machines

Chen, P.-H., Fan, R.-E., & Lin, C. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875973 · Cited by 342 · see citing works

Training v-Support Vector Regression: Theory and Algorithms

Chang, C.-C., & Lin, C. (2002) · Neural Computation · doi:10.1162/089976602760128081 · Cited by 317 · see citing works

Convergence of a Generalized SMO Algorithm for SVM Classifier Design

Keerthi, S. S., & Gilbert, É. (2002) · Machine Learning · doi:10.1023/a:1012431217818 · Cited by 307 · see citing works

A Simple Decomposition Method for Support Vector Machines

Hsu, C., & Lin, C. (2002) · Machine Learning · doi:10.1023/a:1012427100071 · Cited by 301 · see citing works

Neurocomputing : algorithms, architectures and applications

Fogelman‐Soulié, F., & Hérault, J. (1990) · Springer eBooks · Cited by 275 · see citing works

Building Support Vector Machines with Reduced Classifier Complexity

Keerthi, S. S., Chapelle, O., & DeCoste, D. (2006) · MPG.PuRe (Max Planck Society) · doi:10.5555/1248547.1248602 · Cited by 266 · see citing works

On the convergence of the decomposition method for support vector machines

Lin, C. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.963765 · Cited by 245 · see citing works

The analysis of decomposition methods for support vector machines

Chang, C.-C., Hsu, C., & Lin, C. (2000) · IEEE Transactions on Neural Networks · doi:10.1109/72.857780 · Cited by 163 · see citing works

Support Vector Machine Reference Manual

Saunders, C. J., Mo, W. S., Schölkopf Bottou, L., & Smola, A. J. (1998) · ePrints Soton (University of Southampton) · Cited by 148 · see citing works

Asymptotic convergence of an SMO algorithm without any assumptions

Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.977319 · Cited by 139 · see citing works

A formal analysis of stopping criteria of decomposition methods for support vector machines

Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2002.1031937 · Cited by 85 · see citing works

A geometric interpretation of ν-SVM classifiers

Crisp, D. J., & Burges, C. J. C. (1999) · neural information processing systems · Cited by 83 · see citing works

Fast and Scalable Local Kernel Machines

Segata, N., & Blanzieri, E. (2010) · Unitn Eprints Research (Università Degli Studi di Trento) · Cited by 79 · see citing works

Polynomial-Time Decomposition Algorithms for Support Vector Machines

Hush, D. R., & Scovel, C. (2003) · Machine Learning · doi:10.1023/a:1021877911972 · Cited by 78 · see citing works

Maximum-Gain Working Set Selection for SVMs

Glasmachers, T., & Igel, C. (2006) · Cited by 75 · see citing works

On the convergence of a modified version of SVMlightalgorithm

Palagi, L., & Sciandrone, M. (2005) · Optimization methods & software · doi:10.1080/10556780512331318209 · Cited by 39 · see citing works

Working Set Selection Using Second Order Information for Training Support Vector Machines

FanRong-En, ChenPai-Hsuen, & LinChih-Jen (2005) · Journal of Machine Learning Research · doi:10.5555/1046920.1194907 · Cited by 36 · see citing works

SVM-Optimization and Steepest-Descent Line Search.

Simon, H. U., & List, N. (2009) · Conference on Learning Theory · Cited by 32 · see citing works

Maximum-Gain Working Set Selection for SVMs

GlasmachersTobias, & IgelChristian (2006) · Journal of Machine Learning Research · doi:10.5555/1248547.1248600 · Cited by 28 · see citing works

A Note on the Decomposition Methods for Support Vector Regression

Liao, S.-P., Lin, H.-T., & Lin, C. (2002) · Neural Computation · doi:10.1162/089976602753712936 · Cited by 24 · see citing works

General Polynomial Time Decomposition Algorithms

List, N., & Simon, H. U. (2007) · doi:10.5555/1248659.1248671 · Cited by 18 · see citing works

BDVal: reproducible large-scale predictive model development and validation in high-throughput datasets

Dorff, K. C., Chambwe, N., Srdanovic, M., & Campagne, F. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq463 · Cited by 7 · see citing works

Fast and Scalable Local Kernel Machines

SegataNicola, & BlanzieriEnrico (2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1859915 · Cited by 4 · see citing works

SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367 · see citing works

The WEKA data mining software: an update

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works

Are we ready for autonomous driving? The KITTI vision benchmark suite

Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works

A tutorial on support vector regression

Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works

The Perseus computational platform for comprehensive analysis of (prote)omics data

Tyanova, S., Temu, T., Sinitcyn, P. G., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 7,977 · see citing works

Untitled work

(2012) · Cited by 8,009 · see citing works

The Pascal Visual Object Classes Challenge: A Retrospective

Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works

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 6,861 · see citing works

LIBLINEAR: A Library for Large Linear Classification

Fan, R.-E., Chang, K., Hsieh, C., Xiang-rui, W., & Lin, C. (2008) · doi:10.5555/1390681.1442794 · Cited by 6,680 · see citing works

No-Reference Image Quality Assessment in the Spatial Domain

Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064 · see citing works

The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309 · see citing works

A survey on feature selection methods

Chandrashekar, G., & Sahin, F. (2014) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.11.024 · Cited by 5,189 · see citing works

A detailed analysis of the KDD CUP 99 data set

Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396 · see citing works

A Practical Guide to Support Vector Classication

Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,070 · see citing works

Formal Semantics for Kolmogorov-Arnold Network Representations of Operational GamesPreprint

Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.00826 · Cited by 5,266 · see citing works

The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression

Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303 · see citing works

Extreme learning machine: a new learning scheme of feedforward neural networks

Huang, G.-B., Zhu, Q.-Y., & Siew, C. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1380068 · Cited by 4,248 · see citing works

Machine Learning in Agriculture: A Review

Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441 · see citing works

A Review on Multi-Label Learning Algorithms

Zhang, M.-L., & Zhou, Z. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.39 · Cited by 3,336 · see citing works

Random forest classifier for remote sensing classification

Pal, M. (2005) · International Journal of Remote Sensing · doi:10.1080/01431160412331269698 · Cited by 3,293 · see citing works

Bag-of-visual-words and spatial extensions for land-use classification

Yang, Y., & Newsam, S. D. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869829 · Cited by 3,077 · see citing works

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034 · see citing works

Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2584107 · Cited by 3,005 · see citing works

Dlib-ml: A Machine Learning Toolkit

King, D. E. (2009) · Journal of machine learning research · doi:10.5555/1577069.1755843 · Cited by 2,945 · see citing works

Gene selection and classification of microarray data using random forest

Díaz‐Uriarte, R., & de Andrés, S. Á. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-3 · Cited by 2,924 · see citing works

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926 · see citing works

Linear spatial pyramid matching using sparse coding for image classification

Yang, J., Yu, K., Gong, Y., & Huang, T. S. (2009) · 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.2009.5206757 · Cited by 2,895 · see citing works

Random Features for Large-Scale Kernel Machines

Rahimi, A., & Recht, B. (2007) · Cited by 2,783 · see citing works

Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

Zheng, W.-L., & Lu, B.-L. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2431497 · Cited by 2,477 · see citing works

System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015) · Geoscientific model development · doi:10.5194/gmd-8-1991-2015 · Cited by 2,616 · see citing works

Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes

Huang, G.-B., Chen, L., & Siew, C. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875977 · Cited by 2,614 · see citing works

Multi-Label Classification

Tsoumakas, G., & Katakis, I. (2007) · International Journal of Data Warehousing and Mining · doi:10.4018/jdwm.2007070101 · Cited by 2,512 · see citing works

Bias in error estimation when using cross-validation for model selection

Varma, S., & Simon, R. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-91 · Cited by 2,485 · see citing works

Learning to detect natural image boundaries using local brightness, color, and texture cues

Martin, D. R., Fowlkes, C. C., & Malik, J. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1273918 · Cited by 2,424 · see citing works

A comprehensive survey on support vector machine classification: Applications, challenges and trends

Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.118 · Cited by 2,381 · see citing works

Medical hyperspectral imaging: a review

Lu, G., & Fei, B. (2014) · Journal of Biomedical Optics · doi:10.1117/1.jbo.19.1.010901 · Cited by 2,332 · see citing works

Resting-state connectivity biomarkers define neurophysiological subtypes of depression

Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., et al. (2017) · Nature Medicine · doi:10.1038/nm.4246 · Cited by 2,170 · see citing works

Using AUC and accuracy in evaluating learning algorithms

Huang, J., & Ling, C. X. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.50 · Cited by 2,148 · see citing works

Rich Models for Steganalysis of Digital Images

Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144 · see citing works

BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities

Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl999 · Cited by 2,145 · see citing works

Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts

Sun, L. D., Luo, H., Bu, D., Zhao, G., Yu, K., Zhang, C., Liu, Y., Chen, R., & Zhao, Y. C. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt646 · Cited by 2,110 · see citing works

Deep Learning for Hyperspectral Image Classification: An Overview

Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907932 · Cited by 2,007 · see citing works

Using Fourier transform IR spectroscopy to analyze biological materials

Baker, M. J., Trevisan, J., Bassan, P., Bhargava, R., Butler, H. J., Dorling, K. M., Fielden, P. R., Fogarty, S. W., Fullwood, N. J., Heys, K. A., et al. (2014) · Nature Protocols · doi:10.1038/nprot.2014.110 · Cited by 1,968 · see citing works

Training linear SVMs in linear time

Joachims, T. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150429 · Cited by 1,932 · see citing works

Machine learning algorithm validation with a limited sample size

Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0224365 · Cited by 1,901 · see citing works

Multi-task connectivity reveals flexible hubs for adaptive task control

Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013) · Nature Neuroscience · doi:10.1038/nn.3470 · Cited by 1,704 · see citing works

CatBoost for big data: an interdisciplinary review

Hancock, J., & Khoshgoftaar, T. M. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00369-8 · Cited by 1,916 · see citing works

kernlab - An S4 Package for Kernel Methods in R

Karatzoglou, A., Smola, A. J., Hornik, K., & Zeileis, A. (2004) · Journal of Statistical Software · doi:10.18637/jss.v011.i09 · Cited by 1,790 · see citing works

DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.312 · Cited by 1,876 · see citing works

The Google Similarity Distance

Cilibrasi, R., & Vitanyi, P. M. B. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.48 · Cited by 1,775 · see citing works

Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality

Moorthy, A. K., & Bovik, A. C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2147325 · Cited by 1,763 · see citing works

Financial time series forecasting using support vector machines

Kim, K.-J. (2003) · Neurocomputing · doi:10.1016/s0925-2312(03)00372-2 · Cited by 1,734 · see citing works

Machine-learning-assisted materials discovery using failed experiments

Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727 · see citing works

Graph-based approach for airborne light detection and ranging segmentation

Vilariño, D. L., Cabaleiro, J. C., Sánchez, J. M., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,760 · see citing works

Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Lu, L., Niu, Z., Huang, X., Fu, H., Liu, S., et al. (2012) · International Journal of Remote Sensing · doi:10.1080/01431161.2012.748992 · Cited by 1,707 · see citing works

A Multimodal Database for Affect Recognition and Implicit Tagging

Soleymani, M., Lichtenauer, J., Pun, T., & Pantic, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.25 · Cited by 1,664 · see citing works

Orange: data mining toolbox in python

Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., et al. (2013) · Journal of Machine Learning Research · Cited by 1,651 · see citing works

CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features

Kang, Y.-J., Yang, D.-C., Kong, L., Hou, M. L., Meng, Y.-Q., Wei, L., & Gao, G. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx428 · Cited by 1,619 · see citing works

Invariant Scattering Convolution Networks

Bruna, J., & Mallat, S. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.230 · Cited by 1,633 · see citing works

Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

Keerthi, S. S., & Lin, C. (2003) · Neural Computation · doi:10.1162/089976603321891855 · Cited by 1,623 · see citing works

Probability Estimates for Multi-class Classification by Pairwise Coupling

Wu, T., Lin, C., & Weng, R. C. (2004) · Journal of Machine Learning Research · doi:10.5555/1005332.1016791 · Cited by 1,673 · see citing works

A systematic literature review of machine learning methods applied to predictive maintenance

Carvalho, T. P., Soares, F. A. A. D. M. N., Vita, R. O., Francisco, R. D. P., Basto, J., & Alcalá, S. G. S. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2019.106024 · Cited by 1,821 · see citing works

Weisfeiler-Lehman Graph Kernels

Shervashidze, N., Schweitzer, P., van Leeuwen, E. J., Mehlhorn, K., & Borgwardt, K. M. (2010) · Max Planck Digital Library · Cited by 1,608 · see citing works

Large scale metric learning from equivalence constraints

Köstinger, M., Hirzer, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2012) · 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.2012.6247939 · Cited by 1,626 · see citing works

Road crack detection using deep convolutional neural network

Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533052 · Cited by 1,602 · see citing works

Toward Open Set Recognition

Scheirer, W. J., de Rezende Rocha, A., Sapkota, A., & Boult, T. E. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.256 · Cited by 1,573 · see citing works

Support Vector Machines: Theory and Applications

Wang, L. (2005) · Studies in fuzziness and soft computing · doi:10.1007/b95439 · Cited by 1,550 · see citing works

The pyramid match kernel: discriminative classification with sets of image features

Grauman, K. L., & Darrell, T. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.239 · Cited by 1,500 · see citing works

Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild

Li, S., Deng, W., & Du, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.277 · Cited by 1,470 · see citing works

Mining Multi-label Data

Tsoumakas, G., Katakis, I., & Vlahavas, I. P. (2009) · Springer eBooks · doi:10.1007/978-0-387-09823-4_34 · Cited by 1,466 · see citing works

Network‐based classification of breast cancer metastasis

Chuang, H., Lee, E., Liu, Y., Lee, D., & Ideker, T. (2007) · Molecular Systems Biology · doi:10.1038/msb4100180 · Cited by 1,431 · see citing works

Representing shape with a spatial pyramid kernel

Bosch, A., Zisserman, A., & Muñoz, X. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282340 · Cited by 1,407 · see citing works

The cellular and molecular origin of tumor-associated macrophages

Franklin, R. A., Liao, W., Sarkar, A., Kim, M. V., Bivona, M. R., Liu, K., Pamer, E. G., & Li, M. O. (2014) · Science · doi:10.1126/science.1252510 · Cited by 1,399 · see citing works

Working Set Selection Using Second Order Information for Training Support Vector Machines

Fan, R.-E., Chen, P.-H., & Lin, C. (2005) · Journal of Machine Learning Research · Cited by 1,409 · see citing works

Three things everyone should know to improve object retrieval

Arandjelović, R., & Zisserman, A. (2012) · 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.2012.6248018 · Cited by 1,391 · see citing works

A GA-based feature selection and parameters optimizationfor support vector machines

Huang, C.-L., & Wang, C.-J. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.09.024 · Cited by 1,365 · see citing works

Factorization Machines with libFM

Rendle, S. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2168752.2168771 · Cited by 1,345 · see citing works

On the use of cross-validation for time series predictor evaluation

Bergmeir, C., & Benítez, J. M. (2012) · Information Sciences · doi:10.1016/j.ins.2011.12.028 · Cited by 1,337 · see citing works

Multimodal classification of Alzheimer's disease and mild cognitive impairment

Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.008 · Cited by 1,312 · see citing works

Evaluation of local spatio-temporal features for action recognition

Wang, H., Ullah, M. M., Kläser, A., Laptev, I., & Schmid, C. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.124 · Cited by 1,295 · see citing works

Advances in Spectral-Spatial Classification of Hyperspectral Images

Fauvel, M., Tarabalka, Y., Benediktsson, J. A., Chanussot, J., & Tilton, J. C. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2197589 · Cited by 1,288 · see citing works

SemEval-2016 Task 5: Aspect Based Sentiment Analysis

Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al‐Ayyoub, M., Zhao, Y., Qin, B., Clercq, O. D., et al. (2016) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s16-1002 · Cited by 1,287 · see citing works

Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research

Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284 · see citing works

Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks

Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., & Grohe, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014602 · Cited by 172 · see citing works

Recent developments in openSMILE, the munich open-source multimedia feature extractor

Eyben, F., Weninger, F. J., Gross, F., & Schuller, B. W. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502224 · Cited by 1,265 · see citing works

Spectral-spatial classification of hyperspectral data based on deep belief network

Chen, Y., Zhao, X., & Jia, X. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2388577 · Cited by 1,259 · see citing works

The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic

Signorini, A., Segre, A. M., & Polgreen, P. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019467 · Cited by 1,234 · see citing works

Deep supervised, but not unsupervised, models may explain IT cortical representation

Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204 · see citing works

The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays

Li, Z., Askim, J. R., & Suslick, K. S. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00226 · Cited by 1,191 · see citing works

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Dührkop, K., Shen, H., Meusel, M., Rousu, J., & Böcker, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509788112 · Cited by 1,190 · see citing works

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.03.028 · Cited by 1,162 · see citing works

SemEval-2015 Task 12: Aspect Based Sentiment Analysis

Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., & Androutsopoulos, I. (2015) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s15-2082 · Cited by 1,152 · see citing works

Deep Graph Kernels

Yanardag, P., & Vishwanathan, S. V. N. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783417 · Cited by 1,108 · see citing works

Support vector machines for classification in remote sensing

Pal, M., & Mather, P. M. (2005) · International Journal of Remote Sensing · doi:10.1080/01431160512331314083 · Cited by 1,094 · see citing works

Cross-validation pitfalls when selecting and assessing regression and classification models

Krstajic, D., Buturovic, L. J., Leahy, D. E., & Thomas, S. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-10 · Cited by 1,090 · see citing works

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

Peherstorfer, B., Willcox, K. E., & Gunzburger, M. (2018) · SIAM Review · doi:10.1137/16m1082469 · Cited by 1,080 · see citing works

Ensemble Classifiers for Steganalysis of Digital Media

Kodovský, J., Fridrich, J., & Holub, V. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2175919 · Cited by 1,072 · see citing works

Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds

Salmina, E. S., Haider, N., & Tetko, I. V. (2015) · Molecules · doi:10.3390/molecules21010001 · Cited by 1,037 · see citing works

A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities

Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2001)

LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm

Chang, C.-C., & Lin, C.-J. (2001)

LIBSVM : a library for support vector machines," 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

Chang, C.-C., & Lin, C.-J. (2015)

C.-J.: Libsvm: a library for support vector machines

Chang, C.-C., & Lin, C.-J. (2004)

LIBSVM : a libraly for support vector machines

Chung, C.-L. (2001) · Cited by 6 · see citing works