Pattern Recognition with Support Vector Machines
First International Workshop, SVM 2002, Niagara Falls, Canada, August 10, 2002. Proceedings
Published on 29. July 2002
Book
Paperback/Softback
XII, 428 pages
978-3-540-44016-1 (ISBN)
Description
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 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.
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.
More details
Series
Edition
2002 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XII, 428 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 24 mm
Weight
660 gr
ISBN-13
978-3-540-44016-1 (9783540440161)
DOI
10.1007/3-540-45665-1
Schweitzer Classification
Other editions
Additional editions
Seong-Whan Lee | Alessandro Verri
Pattern Recognition with Support Vector Machines
First International Workshop, SVM 2002, Niagara Falls, Canada, August 10, 2002. Proceedings
E-Book
08/2003
Springer
€53.49
Available for download
Content
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 Classification.- 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.