Pattern Recognition with Support Vector Machines [electronic resource] : First International Workshop, SVM 2002 Niagara Falls, Canada, August 10, 2002 Proceedings / edited by Seong-Whan Lee, Alessandro Verri.Material type: TextLanguage: English Series: Lecture Notes in Computer Science: 2388Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2002Description: XII, 428 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540456650Subject(s): Computer science | Computer software | Artificial intelligence | Computer vision | Optical pattern recognition | Mathematical statistics | Computer Science | Pattern Recognition | Image Processing and Computer Vision | Algorithm Analysis and Problem Complexity | Computation by Abstract Devices | Artificial Intelligence (incl. Robotics) | Statistics and Computing/Statistics ProgramsAdditional physical formats: Printed edition:: No titleDDC classification: 006.4 LOC classification: Q337.5TK7882.P3Online resources: Click here to access online
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-Class SVM 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.
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.