Pattern Recognition Support Vector (29 results)
Pattern Recognition With Support Vector Machines : First International Workshop, Svm 2002, Niagara Falls, Canada, August 10, 2002 : Proceedings
Svm 200 (2002 Niagara Falls, Ont.); Verri, Alessandro (EDT); Lee, Seong-Whan (EDT)
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Paperback or Softback. Condition: New. Pattern Recognition with Support Vector Machines: First International Workshop, Svm 2002, Niagara Falls, Canada, August 10, 2002. Proceedings. Book.
Pattern Recognition With Support Vector Machines : First International Workshop, Svm 2002, Niagara Falls, Canada, August 10, 2002 : Proceedings
Svm 200 (2002 Niagara Falls, Ont.); Verri, Alessandro (EDT); Lee, Seong-Whan (EDT)
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Paperback. Condition: New. 2002 ed. With their introduction in 1995, Support Vector Machines (SVMs) marked the beginningofanewerainthelearningfromexamplesparadigm.Rootedinthe Statistical Learning Theory developed by Vladimir Vapnik at ATandT, SVMs quickly gained attention from the pattern recognition community due to a n- beroft…heoreticalandcomputationalmerits.Theseinclude,forexample,the simple geometrical interpretation of the margin, uniqueness of the solution, s- tistical robustness of the loss function, modularity of the kernel function, and over?t control through the choice of a single regularization parameter. Like all really good and far reaching ideas, SVMs raised a number of - terestingproblemsforboththeoreticiansandpractitioners.Newapproachesto Statistical Learning Theory are under development and new and more e?cient methods for computing SVM with a large number of examples are being studied. Being interested in the development of trainable systems ourselves, we decided to organize an international workshop as a satellite event of the 16th Inter- tional Conference on Pattern Recognition emphasizing the practical impact and relevance of SVMs for pattern recognition.By March 2002, a total of 57 full papers had been submitted from 21 co- tries.Toensurethehighqualityofworkshopandproceedings,theprogramc- mitteeselectedandaccepted30ofthemafterathoroughreviewprocess.Ofthese papers16werepresentedin4oralsessionsand14inapostersession.Thepapers span a variety of topics in pattern recognition with SVMs from computational theoriestotheirimplementations.Inadditiontotheseexcellentpresentations, there were two invited papers by Sayan Mukherjee, MIT and Yoshua Bengio, University of Montreal.
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Published by Chapman and Hall/CRC, 2020
Series: Chapman & Hall/Crc Machine Learning & Pattern Recognition, Book 7 of 15. Book 7 of 15 - Chapman & Hall/Crc Machine Learning & Pattern Recognition
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Pattern Recognition With Support Vector Machines : First International Workshop, Svm 2002, Niagara Falls, Canada, August 10, 2002 : Proceedings
Svm 200 (2002 Niagara Falls, Ont.); Verri, Alessandro (EDT); Lee, Seong-Whan (EDT)
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Published by Chapman and Hall/CRC, 2020
Series: Chapman & Hall/Crc Machine Learning & Pattern Recognition, Book 7 of 15. Book 7 of 15 - Chapman & Hall/Crc Machine Learning & Pattern Recognition
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Pattern Recognition With Support Vector Machines : First International Workshop, Svm 2002, Niagara Falls, Canada, August 10, 2002 : Proceedings
Svm 200 (2002 Niagara Falls, Ont.); Verri, Alessandro (EDT); Lee, Seong-Whan (EDT)
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Taschenbuch. Condition: Neu. Pattern Recognition with Support Vector Machines | First International Workshop, SVM 2002, Niagara Falls, Canada, August 10, 2002. Proceedings | Seong-Whan Lee (u. a.) | Taschenbuch | xii | Englisch | 2002 | Springer | EAN 9783540440161 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tierg…artenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - With their introduction in 1995, Support Vector Machines (SVMs) marked the beginningofanewerainthelearningfromexamplesparadigm.Rootedinthe Statistical Learning Theory developed by Vladimir Vapnik at AT&T, SVMs quickly gained attention from the patt…ern recognition community due to a n- beroftheoreticalandcomputationalmerits.Theseinclude,forexample,the simple geometrical interpretation of the margin, uniqueness of the solution, s- tistical robustness of the loss function, modularity of the kernel function, and over t control through the choice of a single regularization parameter. Like all really good and far reaching ideas, SVMs raised a number of - terestingproblemsforboththeoreticiansandpractitioners.Newapproachesto Statistical Learning Theory are under development and new and more e cient methods for computing SVM with a large number of examples are being studied. Being interested in the development of trainable systems ourselves, we decided to organize an international workshop as a satellite event of the 16th Inter- tional Conference on Pattern Recognition emphasizing the practical impact and relevance of SVMs for pattern recognition. By March 2002, a total of 57 full papers had been submitted from 21 co- tries.Toensurethehighqualityofworkshopandproceedings,theprogramc- mitteeselectedandaccepted30ofthemafterathoroughreviewprocess.Ofthese papers16werepresentedin4oralsessionsand14inapostersession.Thepapers span a variety of topics in pattern recognition with SVMs from computational theoriestotheirimplementations.Inadditiontotheseexcellentpresentations, there were two invited papers by Sayan Mukherjee, MIT and Yoshua Bengio, University of Montreal.
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Published by Chapman and Hall/CRC, 2014
Series: Chapman & Hall/Crc Machine Learning & Pattern Recognition, Book 7 of 15. Book 7 of 15 - Chapman & Hall/Crc Machine Learning & Pattern Recognition
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Published by Springer, 2010
Series: Advances in Computer Vision and Pattern Recognition, Book 13 of 86. Book 13 of 86 - Advances in Computer Vision and Pattern Recognition
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Published by Chapman and Hall/CRC, 2014
Series: Chapman & Hall/Crc Machine Learning & Pattern Recognition, Book 7 of 15. Book 7 of 15 - Chapman & Hall/Crc Machine Learning & Pattern Recognition
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Series: Advances in Computer Vision and Pattern Recognition, Book 13 of 86. Book 13 of 86 - Advances in Computer Vision and Pattern Recognition
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Published by Springer, 2012
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -With their introduction in 1995, Support Vector Machines (SVMs) marked the beginningofanewerainthelearningfromexamplesparadigm.Rootedinthe Statistical Learning Theory developed by Vladimir Vapnik at AT&T, SVMs quickly gained attenti…on from the pattern recognition community due to a n- beroftheoreticalandcomputationalmerits.Theseinclude,forexample,the simple geometrical interpretation of the margin, uniqueness of the solution, s- tistical robustness of the loss function, modularity of the kernel function, and over t control through the choice of a single regularization parameter. Like all really good and far reaching ideas, SVMs raised a number of - terestingproblemsforboththeoreticiansandpractitioners.Newapproachesto Statistical Learning Theory are under development and new and more e cient methods for computing SVM with a large number of examples are being studied. Being interested in the development of trainable systems ourselves, we decided to organize an international workshop as a satellite event of the 16th Inter- tional Conference on Pattern Recognition emphasizing the practical impact and relevance of SVMs for pattern recognition. By March 2002, a total of 57 full papers had been submitted from 21 co- tries.Toensurethehighqualityofworkshopandproceedings,theprogramc- mitteeselectedandaccepted30ofthemafterathoroughreviewprocess.Ofthese papers16werepresentedin4oralsessionsand14inapostersession.Thepapers span a variety of topics in pattern recognition with SVMs from computational theoriestotheirimplementations.Inadditiontotheseexcellentpresentations, there were two invited papers by Sayan Mukherjee, MIT and Yoshua Bengio, University of Montreal. 438 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Invited Papers.- Predicting Signal Peptides with Support Vector Machines.- Scaling Large Learning Problems with Hard Parallel Mixtures.- Computational Issues.- On the Generalization of Kernel Machines.- Kernel Whiteni…ng for One-Class Classification.- A Fast.
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Invited Papers.- Predicting Signal Peptides with Support Vector Machines.- Scaling Large Learning Problems with Hard Parallel Mixtures.- Computational Issues.- On the Generalization of Kernel Machines.- Kernel Whitening for One-Class Cl…assification.- A Fast SVM Training Algorithm.- Support Vector Machines with Embedded Reject Option.- Object Recognition.- Image Kernels.- Combining Color and Shape Information for Appearance-Based Object Recognition Using Ultrametric Spin Glass-Markov Random Fields.- Maintenance Training of Electric Power Facilities Using Object Recognition by SVM.- Kerneltron: Support Vector 'Machine' in Silicon.- Pattern Recognition.- Advances in Component-Based Face Detection.- Support Vector Learning for Gender Classification Using Audio and Visual Cues: A Comparison.- Analysis of Nonstationary Time Series Using Support Vector Machines.- Recognition of Consonant-Vowel (CV) Units of Speech in a Broadcast News Corpus Using Support Vector Machines.- Applications.- Anomaly Detection Enhanced Classification in Computer Intrusion Detection.- Sparse Correlation Kernel Analysis and Evolutionary Algorithm-Based Modeling of the Sensory Activity within the Rat's Barrel Cortex.- Applications of Support Vector Machines for Pattern Recognition: A Survey.- Typhoon Analysis and Data Mining with Kernel Methods.- Poster Papers.- Support Vector Features and the Role of Dimensionality in Face Authentication.- Face Detection Based on Cost-Sensitive Support Vector Machines.- Real-Time Pedestrian Detection Using Support Vector Machines.- Forward Decoding Kernel Machines: A Hybrid HMM/SVM Approach to Sequence Recognition.- Color Texture-Based Object Detection: An Application to License Plate Localization.- Support Vector Machines in Relational Databases.- Multi-ClassSVM Classifier Based on Pairwise Coupling.- Face Recognition Using Component-Based SVM Classification and Morphable Models.- A New Cache Replacement Algorithm in SMO.- Optimization of the SVM Kernels Using an Empirical Error Minimization Scheme.- Face Detection Based on Support Vector Machines.- Detecting Windows in City Scenes.- Support Vector Machine Ensemble with Bagging.- A Comparative Study of Polynomial Kernel SVM Applied to Appearance-Based Object Recognition.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 438 pp. Englisch.
