A Practical Guide to Support Vector Classication

Keywords

Support vector machine, Computer science, Artificial intelligence, Simple (philosophy), Machine learning

References

• A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type MethodsHsuan-Tien Lin — 2005
• A training algorithm for optimal margin classifiersBernhard E. Boser, Isabelle Guyon, Vladimir Vapnik — 1992
• Asymptotic Behaviors of Support Vector Machines with Gaussian KernelS. Sathiya Keerthi, Chih‐Jen Lin — 2003
• LIBLINEAR: A Library for Large Linear ClassificationRong-En Fan, Kai‐Wei Chang, Cho‐Jui Hsieh — 2008
• Support-Vector NetworksCorinna Cortes, Vladimir Vapnik — 1995
• Trust region Newton methods for large-scale logistic regressionChih‐Jen Lin, Ruby C. Weng, S. Sathiya Keerthi — 2007
• Machine Learning, Neural and Statistical ClassificationBill Fulkerson, D. Michie, D. J. Spiegelhalter — 2009
• PSORT-B: improving protein subcellular localization prediction for Gram-negative bacteriaJennifer L. Gardy — 2003
• The Nature of Statistical Learning TheoryVladimir Vapnik — 2000
• The Nature of Statistical Learning TheoryVladimir Vapnik — 1995

Cited by

• Software defect prediction using static code metrics : formulating a methodologyDavid Philip Harry Gray — 2013
• Machine Learning for First-Order Theorem ProvingJames P. Bridge, Sean B. Holden, Lawrence C. Paulson — 2014
• Exploring Low Cost Laser Sensors to Identify Flying Insect SpeciesDiego Furtado Silva, Vinícius M. A. Souza, Daniel P. W. Ellis — 2015
• Detection of web based command & control channelsMartin Warmer — 2011
• A user's guide to support vector machines.Asa Ben‐Hur, Jason Weston — 2010
• Detecting driver distractionYulan Liang — 2009
• Learning-based fusion for data deduplication: A robust and automated solutionJ. Dinerstein — 2010
• Toward a Solution for "Genetic Algorithm"-"Support Vector Machine" Combination to Have a Reliable P300-based BCIGholamreza Salimi Khorshidi, Ayyoub Jaafari, Ali Motie Nasrabadi — 2007
• Predicting gene function using few positive examples and unlabeled onesVincent Boudon, J.P. Champion, Tony Gabard — 2010
• Détection multimodale du stress pour la conception de logiciels de remédiation. (Multimodal stress detection for remediation software design)Mariette Soury — 2014
• Automatic Multi-model-Based Segmentation of the Left Atrium in Cardiac MRI ScansBo Malmberg — 2012
• Diagnosis of Hashimoto’s thyroiditis in ultrasound using tissue characterization and pixel classificationRamona Chube — 2013
• Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information SystemQing Duan — 2014
• A medical diagnostic tool based on radial basis function classifiers and evolutionary simulated annealingТ. В. Бурнышева, В. О. Каледин, И. В. Равковская — 2014
• Novel Cascade FPGA Accelerator for Support Vector Machines ClassificationJames Ladyman, Don Ross — 2012
• Easy Hyperparameter Search Using OptunityMarc Claesen, Jaak Simm, Dušan Popović — 2014
• In Vivo Evidence Of Neurophysiological Maturation of the Human Adolescent Striatumアラン 金 — 2014
• The Construction of Support Vector Machine Classifier Using the Firefly AlgorithmIna Belicka, Mara Viklante, Maris Arbidans — 2015
• Efficient sampling-based Rbdo by using virtual support vector machine and improving the accuracy of the Kriging methodHyeongjin Song — 2013
• Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imagingMichel Coulombe — 2015

Related papers

• Random ForestsLeo Breiman — 2001
• The Nature of Statistical Learning TheoryVladimir Vapnik — 1995
• LIBSVM: A library for support vector machinesChih-Chung Chang, Chih‐Jen Lin — 2011
• Statistical learning theoryYuhai Wu, Vladimir Vapnik — 1998
• A Tutorial on Support Vector Machines for Pattern RecognitionChristopher J. C. Burges — 1998
• The WEKA data mining software: an updateMark Hall, Eibe Frank, Geoffrey Holmes — 2009
• Support-Vector NetworksCorinna Cortes, Vladimir Vapnik — 1995