Cited by 13,855Machine Learning and AlgorithmsMachine Learning and ELMStochastic Gradient Optimization Techniques

The DOI matches its Crossref record.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Open access

Cristianini, Nello, Shawe-Taylor, John·2000·Cambridge University Press eBooks

Open this paper in CoChat — free account

Abstract

This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

Integrity signals

Registry

Crossref · 10.1017/cbo9780511801389

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Sorted by citations

Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Artificial intelligence for fault diagnosis of rotating machinery: A review
Liu, R., Yang, B., Zio, E., & Chen, X. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.02.016 · Cited by 2,188
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
Machine learning for medical imaging
Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017) · RadioGraphics · doi:10.1148/rg.2017160130 · Cited by 1,672
Machine learning: a review of classification and combining techniques
Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006) · Artificial Intelligence Review · doi:10.1007/s10462-007-9052-3 · Cited by 1,582
Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram
Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0240-2 · Cited by 1,389
Adversarial machine learning
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.103 · Cited by 1,154
Forecasting stock market movement direction with support vector machine
Huang, W., Nakamori, Y., & Wang, S.-Y. (2005) · Computers & Operations Research · doi:10.1016/j.cor.2004.03.016 · Cited by 1,119
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
Data mining for credit card fraud: a comparative study
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.008 · Cited by 1,108
A review of feature selection methods on synthetic data
Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-012-0487-8 · Cited by 830
Predictive data mining in clinical medicine: Current issues and guidelines
BELLAZZI, R., & ZUPAN, B. (2008) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2006.11.006 · Cited by 823
Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands
Atzori, M., Cognolato, M., & Müller, H. (2016) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2016.00009 · Cited by 684
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
Wang, J.-X., Wu, J.-L., & Xiao, H. (2017) · Physical Review Fluids · doi:10.1103/physrevfluids.2.034603 · Cited by 737
Exploiting machine learning for end-to-end drug discovery and development
Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials · doi:10.1038/s41563-019-0338-z · Cited by 578
Handcrafted vs. non-handcrafted features for computer vision classification
Nanni, L., Ghidoni, S., & Brahnam, S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.025 · Cited by 542
Real-time detection of driver cognitive distraction using support vector machines
Liang, Y., Reyes, M. L., & Lee, J. D. (2007) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2007.895298 · Cited by 482
Learning-based approach for online lane change intention prediction
Kumar, P., Perrollaz, M., Lefevre, S., & Laugier, C. (2013) · 2013 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2013.6629564 · Cited by 410
Data mining and machine learning in astronomy
BALL, N. M., & BRUNNER, R. J. (2010) · International Journal of Modern Physics D · doi:10.1142/s0218271810017160 · Cited by 383
Inuc-pseknc: A sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition
Guo, S.-H., Deng, E.-Z., Xu, L.-Q., Ding, H., Lin, H., Chen, W., & Chou, K.-C. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu083 · Cited by 375
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
Mansouri, K., Abdelaziz, A., Rybacka, A., Roncaglioni, A., Tropsha, A., Varnek, A., Zakharov, A., Worth, A., Richard, A. M., Grulke, C. M., et al. (2016) · Environmental Health Perspectives · doi:10.1289/ehp.1510267 · Cited by 375
“…spectral data–activity relationship (QSDAR) (Beger et al. 2001; Beger and Wilkes 2001; Slavov et al. 2013), support vector machines (SVM) (Cristianini and Shawe-Taylor 2000), k nearest neighbors (kNN) (Cover and Hart 1967; Kowalski and Bender 1972), associative artificial neural…”
Viewpoint: Human-in-the-loop Artificial Intelligence
Zanzotto, F. M. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11345 · Cited by 380
Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment
Burgos-Robles, A., Kimchi, E. Y., Izadmehr, E. M., Porzenheim, M. J., Ramos-Guasp, W. A., Nieh, E. H., Felix-Ortiz, A. C., Namburi, P., Leppla, C. A., Presbrey, K. N., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4553 · Cited by 318
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01073 · Cited by 337
Batch effect removal methods for microarray gene expression data integration: a survey
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter, C., Weiss-Solis, D. Y., Duque, R., Bersini, H., & Nowe, A. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs037 · Cited by 333
Inductive Confidence Machines for Regression
Papadopoulos, H., Proedrou, K., Vovk, V., & Gammerman, A. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-36755-1_29 · Cited by 346
Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images
Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.066 · Cited by 319
Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques
Monte-Moreno, E. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.05.001 · Cited by 311
Transaction aggregation as a strategy for credit card fraud detection
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-008-0116-z · Cited by 337
iRNA-PseU: Identifying RNA pseudouridine sites
Chen, W., Tang, H., Ye, J., Lin, H., & Chou, K.-C. (2016) · Molecular Therapy - Nucleic Acids · doi:10.1038/mtna.2016.37 · Cited by 291
Support Vector Machines for TCP traffic classification
Este, A., Gringoli, F., & Salgarelli, L. (2009) · Computer Networks · doi:10.1016/j.comnet.2009.05.003 · Cited by 270
Genetic algorithms for computational materials discovery accelerated by machine learning
Jennings, P. C., Lysgaard, S., Hummelshøj, J. S., Vegge, T., & Bligaard, T. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0181-4 · Cited by 269
Early diagnosis of Alzheimer?s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images
Khedher, L., Ramírez, J., Górriz, J. M., Brahim, A., & Segovia, F. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.09.072 · Cited by 262
Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis
Petrantonakis, P. C., & Hadjileontiadis, L. J. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.7 · Cited by 260
A Powerful and Flexible Multilocus Association Test for Quantitative Traits
Kwee, L. C., Liu, D., Lin, X., Ghosh, D., & Epstein, M. P. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2007.10.010 · Cited by 227
New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships
Jain, A., Hautier, G., Ong, S. P., & Persson, K. (2016) · Journal of Materials Research · doi:10.1557/jmr.2016.80 · Cited by 244
Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach
Liu, B., Fang, L., Liu, F., Wang, X., Chen, J., & Chou, K.-C. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121501 · Cited by 238
Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA
Kakadiaris, I. A., Vrigkas, M., Yen, A. A., Kuznetsova, T., Budoff, M., & Naghavi, M. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.009476 · Cited by 227
A Framework for Automatic Human Emotion Classification Using Emotion Profiles
Mower, E., Mataric, M. J., & Narayanan, S. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2076804 · Cited by 215
Pedestrian Recognition and Tracking Using 3D LiDAR for Autonomous Vehicle
Wang, H., Wang, B., Liu, B., Meng, X., & Yang, G. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.11.014 · Cited by 209
A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization
Dai, Y., & Zhao, P. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115332 · Cited by 202
Detecting Cars in UAV Images With a Catalog-Based Approach
Moranduzzo, T., & Melgani, F. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2296351 · Cited by 200
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
Detecting Clusters of Fake Accounts in Online Social Networks
Xiao, C., Freeman, D. M., & Hwa, T. (2015) · Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/2808769.2808779 · Cited by 184
M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning
Wei, L., Chen, H., & Su, R. (2018) · Molecular Therapy Nucleic Acids · doi:10.1016/j.omtn.2018.07.004 · Cited by 179
Privacy-preserving SVM classification
Vaidya, J., Yu, H., & Jiang, X. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0073-7 · Cited by 178
Text Categorization Models for High-Quality Article Retrieval in Internal Medicine
Aphinyanaphongs, Y. (2004) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m1641 · Cited by 177
Combining EEG signal processing with supervised methods for Alzheimer’s patients classification
Fiscon, G., Weitschek, E., Cialini, A., Felici, G., Bertolazzi, P., De Salvo, S., Bramanti, A., Bramanti, P., & De Cola, M. C. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0613-y · Cited by 172
High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge
Patrick, J., & Li, M. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2010.003939 · Cited by 159
Toward an AI Era: Advances in Electronic Skins
Fu, X., Cheng, W., Wan, G., Yang, Z., & Tee, B. C. K. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00049 · Cited by 172
Application of wrapper approach and composite classifier to the stock trend prediction
HUANG, C., YANG, D., & CHUANG, Y. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.05.035 · Cited by 167
Mobile Human Airbag System for Fall Protection Using MEMS Sensors and Embedded SVM Classifier
Shi, G., Chan, C. S., Li, W. J., Leung, K.-S., Zou, Y., & Jin, Y. (2009) · IEEE Sensors Journal · doi:10.1109/jsen.2008.2012212 · Cited by 162
Interpretable classification models for recidivism prediction
Zeng, J., Ustun, B., & Rudin, C. (2017) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12227 · Cited by 220
Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients
Ahlrichs, C., Samà, A., Lawo, M., Cabestany, J., Rodríguez-Martín, D., Pérez-López, C., Sweeney, D., Quinlan, L. R., Laighin, G. Ò., Counihan, T., et al. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1395-3 · Cited by 128
KISS: Stochastic Packet Inspection Classifier for UDP Traffic
Finamore, A., Mellia, M., Meo, M., & Rossi, D. (2010) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2010.2044046 · Cited by 130
Clinical text classification research trends: Systematic literature review and open issues
Mujtaba, G., Shuib, L., Idris, N., Hoo, W. L., Raj, R. G., Khowaja, K., Shaikh, K., & Nweke, H. F. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.034 · Cited by 123
Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques
Knudby, A., LeDrew, E., & Brenning, A. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.01.007 · Cited by 129
User Verification Leveraging Gait Recognition for Smartphone Enabled Mobile Healthcare Systems
Ren, Y., Chen, Y., Chuah, M. C., & Yang, J. (2015) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2014.2365185 · Cited by 123
Designing constraints: composing and performing with digital musical systems
Magnusson, T. (2010) · Computer Music Journal · doi:10.1162/comj_a_00026 · Cited by 123
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
The neural basis for a persistent internal state in Drosophila females
Deutsch, D., Pacheco, D., Encarnacion-Rivera, L., Pereira, T., Fathy, R., Clemens, J., Girardin, C., Calhoun, A., Ireland, E., Burke, A., et al. (2020) · eLife · doi:10.7554/elife.59502 · Cited by 115
Semiautonomous Vehicular Control Using Driver Modeling
Shia, V. A., Gao, Y., Vasudevan, R., Campbell, K. D., Lin, T., Borrelli, F., & Bajcsy, R. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2325776 · Cited by 114
End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study
Coppock, H., Gaskell, A., Tzirakis, P., Baird, A., Jones, L., & Schuller, B. (2021) · BMJ Innovations · doi:10.1136/bmjinnov-2021-000668 · Cited by 108
Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods
Lise, S., Archambeau, C., Pontil, M., & Jones, D. T. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-365 · Cited by 107
The taste of music
Mesz, B., Trevisan, M. A., & Sigman, M. (2011) · Perception · doi:10.1068/p6801 · Cited by 108
An Efficient Classifier for Alzheimer’s Disease Genes Identification
Xu, L., Liang, G., Liao, C., Chen, G.-D., & Chang, C.-C. (2018) · Molecules · doi:10.3390/molecules23123140 · Cited by 107
Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification
Johnston, B. A., Mwangi, B., Matthews, K., Coghill, D., Konrad, K., & Steele, J. D. (2014) · Human Brain Mapping · doi:10.1002/hbm.22542 · Cited by 101
Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images
Wu, W.-J., Lin, S.-W., & Moon, W. K. (2012) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2012.07.004 · Cited by 99
SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
Xu, L., Liang, G., Shi, S., & Liao, C. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19061773 · Cited by 95
Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments
Samà, A., Rodríguez-Martín, D., Pérez-López, C., Català, A., Alcaine, S., Mestre, B., Prats, A., Crespo, M. C., & Bayés, À. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.05.009 · Cited by 76
Human activity recognition based on a sensor weighting hierarchical classifier
Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B., & Valenzuela, O. (2013) · Soft Computing · doi:10.1007/s00500-012-0896-3 · Cited by 79
Predicting Patient Survival after Liver Transplantation Using Evolutionary Multi-Objective Artificial Neural Networks
Cruz-Ramírez, M., Hervás-Martínez, C., Fernández, J. C., Briceño, J., & de la Mata, M. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.02.004 · Cited by 79
A Random Sampling Technique for Training Support Vector Machines: For Primal-Form Maximal-Margin Classifiers
Balcázar, J., Dai, Y., & Watanabe, O. (2001) · Lecture Notes in Computer Science · doi:10.1007/3-540-45583-3_11 · Cited by 71
An approach for extensibly profiling the molecular states of cellular subpopulations
Loo, L.-H., Lin, H.-J., Steininger, R. J., Wang, Y., Wu, L. F., & Altschuler, S. J. (2009) · Nature Methods · doi:10.1038/nmeth.1375 · Cited by 69
Abacus: Accurate behavioral classification of P2P-TV traffic
Bermolen, P., Mellia, M., Meo, M., Rossi, D., & Valenti, S. (2011) · Computer Networks · doi:10.1016/j.comnet.2010.12.004 · Cited by 67
ACO Resampling: Enhancing the performance of oversampling methods for class imbalance classification
Li, M., Xiong, A., Wang, L., Deng, S., & Ye, J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105818 · Cited by 66
Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds
Geppert, H., Horváth, T., Gärtner, T., Wrobel, S., & Bajorath, J. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700461s · Cited by 63
Fusion of Wearable and Contactless Sensors for Intelligent Gesture Recognition
Liang, X., Li, H., Wang, W., Liu, Y., Ghannam, R., Fioranelli, F., & Heidari, H. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900088 · Cited by 64
miRTex: A Text Mining System for miRNA-Gene Relation Extraction
Li, G., Ross, K. E., Arighi, C. N., Peng, Y., Wu, C. H., & Vijay-Shanker, K. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004391 · Cited by 61
Machine learning based soft sensor model for BOD estimation using intelligence at edge
Pattnaik, B. S., Pattanayak, A. S., Udgata, S. K., & Panda, A. K. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00259-9 · Cited by 61
Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment
Gurevich, P., Stuke, H., Kastrup, A., Stuke, H., & Hildebrandt, H. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00114 · Cited by 53
Automatic Chinese Factual Question Generation
Liu, M., Rus, V., & Liu, L. (2017) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2016.2565477 · Cited by 52
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
Lessons and tips for designing a machine learning study using EHR data
Arbet, J., Brokamp, C., Meinzen-Derr, J., Trinkley, K. E., & Spratt, H. M. (2021) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.513 · Cited by 46
Machine-learning models for combinatorial catalyst discovery
Landrum, G. A., Penzotti, J. E., & Putta, S. (2005) · Measurement Science and Technology · doi:10.1088/0957-0233/16/1/035 · Cited by 48
Multiple ROI selection based focal liver lesion classification in ultrasound images
Jeon, J. H., Choi, J. Y., Lee, S., & Ro, Y. M. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.07.053 · Cited by 47
Complex Networks Govern Coiled-Coil Oligomerization – Predicting and Profiling by Means of a Machine Learning Approach
Mahrenholz, C. C., Abfalter, I. G., Bodenhofer, U., Volkmer, R., & Hochreiter, S. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.004994 · Cited by 47
Detection of microcytic hypochromia using cbc and blood film features extracted from convolution neural network by different classifiers
Purwar, S., Tripathi, R. K., Ranjan, R., & Saxena, R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07927-0 · Cited by 46
A comparative study in birds: Call-type-independent species and individual recognition using four machine-learning methods and two acoustic features
Cheng, J., Xie, B., Lin, C., & Ji, L. (2012) · Bioacoustics · doi:10.1080/09524622.2012.669664 · Cited by 42
Detection method of unlearned pattern using support vector machine in damage classification based on deep neural network
Kohiyama, M., Oka, K., & Yamashita, T. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2552 · Cited by 38
Low-energy formulations of support vector machine kernel functions for biomedical sensor applications
Lee, K. H., Kung, S.-Y., & Verma, N. (2012) · Journal of Signal Processing Systems · doi:10.1007/s11265-012-0672-8 · 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
Solving the App-Level Classification Problem of P2P Traffic Via Optimized Support Vector Machines
Wang, R., Liu, Y., Yang, Y., & Zhou, X. (2006) · Sixth International Conference on Intelligent Systems Design and Applications · doi:10.1109/isda.2006.253893 · Cited by 28
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 for the Identification of African Lions from Individual Vocalizations
Trapanotto, M., Nanni, L., Brahnam, S., & Guo, X. (2022) · Journal of Imaging · doi:10.3390/jimaging8040096 · Cited by 24
Employing linear prediction residual signal of wavelet sub-bands in automatic detection of laryngeal pathology
Akbari, A., & Arjmandi, M. K. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.02.008 · Cited by 22
Differential protein expression profiles between Plasmodium falciparum parasites isolated from subjects presenting with pregnancy-associated malaria and uncomplicated malaria in Benin
Bertin, G. I., Sabbagh, A., Guillonneau, F., Jafari-Guemouri, S., Ezinmegnon, S., Federici, C., Hounkpatin, B., Fievet, N., & Deloron, P. (2013) · The Journal of Infectious Diseases · doi:10.1093/infdis/jit377 · Cited by 21
Introduction to Scientific Data Mining: Direct Kernel Methods and Applications
Embrechts, M. J., Szymanski, B., & Sternickel, K. (2004) · Computationally Intelligent Hybrid Systems · doi:10.1002/9780471683407.ch10 · Cited by 21
Support vector classification for fault diagnostics of an electrical machine
Poyhonen, S., Negrea, M., Arkkio, A., Hyotyniemi, H., & Koivo, H. (2003) · 6th International Conference on Signal Processing, 2002. · doi:10.1109/icosp.2002.1180133 · Cited by 20

Authors: Cristianini, N., & Shawe-Taylor, J.
Affiliations: University of Bristol (GB)
Royal Holloway University of London (GB)
ORCID: Cristianini, Nello
Shawe-Taylor, John
Venue: Cambridge University Press eBooks
Publisher: Cambridge University Press
Published: 2000-03-23
DOI: 10.1017/cbo9780511801389
OpenAlex: W1563088657
Cited by: 13,855
References: 0
Citations by year: 2026: 143 · 2025: 261 · 2024: 305 · 2023: 364 · 2022: 429 · 2021: 577 · 2020: 589 · 2019: 614 · 2018: 618 · 2017: 618 · 2016: 651 · 2015: 741 · 2014: 762 · 2013: 726 · 2012: 763
FWCI: 56.63
Type: book
Language: en
Open access: green
infoscience.epfl.ch
Licence: cc-by-nc-nd
www.cambridge.org/core/terms (unspecified, 4730 d)
Locations: Cambridge University Press eBooks (publishedVersion)
Virtual Defense Library (Ministerio de Defensa) (submittedVersion)
International Linear Collider (submittedVersion)
UCL Discovery (University College London) (submittedVersion)
Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
Keywords: support vector machine, kernel methods, statistical learning theory, text classification, handwritten character recognition, image classification, machine learning, data mining

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks