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ORCID1. SVM 2002: Niagara Falls, Canada
- Seong-Whan Lee, Alessandro Verri:
Pattern Recognition with Support Vector Machines, First International Workshop, SVM 2002, Niagara Falls, Canada, August 10, 2002, Proceedings. Lecture Notes in Computer Science 2388, Springer 2002, ISBN 3-540-44016-X
Invited Papers
- Neelanjan Mukherjee, Sayan Mukherjee:
Predicting Signal Peptides with Support Vector Machines. 1-7 - Ronan Collobert, Yoshua Bengio, Samy Bengio:
Scaling Large Learning Problems with Hard Parallel Mixtures. 8-23
Computational Issues
- Pablo Navarrete, Javier Ruiz-del-Solar
:
On the Generalization of Kernel Machines. 24-39 - David M. J. Tax, Piotr Juszczak:
Kernel Whitening for One-Class Classification. 40-52 - Jian-xiong Dong, Adam Krzyzak, Ching Y. Suen:
A Fast SVM Training Algorithm. 53-67 - Giorgio Fumera, Fabio Roli:
Support Vector Machines with Embedded Reject Option. 68-82
Object Recognition
- Annalisa Barla, Emanuele Franceschi, Francesca Odone, Alessandro Verri:
Image Kernels. 83-96 - Barbara Caputo, Gyuri Dorkó, Heinrich Niemann:
Combining Color and Shape Information for Appearance-Based Object Recognition Using Ultrametric Spin Glass-Markov Random Fields. 97-111 - Chikahito Nakajima, Massimiliano Pontil:
Maintenance Training of Electric Power Facilities Using Object Recognition by SVM. 112-119 - Roman Genov, Gert Cauwenberghs:
Kerneltron: Support Vector 'Machine' in Silicon. 120-134
Pattern Recognition
- Stanley M. Bileschi, Bernd Heisele:
Advances in Component-Based Face Detection. 135-143 - L. Walawalkar, Mohammed Yeasin, Anand M. Narasimhamurthy, Rajeev Sharma:
Support Vector Learning for Gender Classification Using Audio and Visual Cues: A Comparison. 144-159 - Ming-Wei Chang, Chih-Jen Lin, Ruby C. Weng:
Analysis of Nonstationary Time Series Using Support Vector Machines. 160-170 - Chellu Chandra Sekhar, Kazuya Takeda, Fumitada Itakura:
Recognition of Consonant-Vowel (CV) Units of Speech in a Broadcast News Corpus Using Support Vector Machines. 171-185
Applications
- Mike Fugate, James R. Gattiker:
Anomaly Detection Enhanced Classification in Computer Intrusion Detection. 186-197 - Mariofanna G. Milanova, Tomasz G. Smolinski, Grzegorz M. Boratyn, Jacek M. Zurada, Andrzej Wróbel:
Sparse Correlation Kernel Analysis and Evolutionary Algorithm-Based Modeling of the Sensory Activity within the Rat's Barrel Cortex. 198-212 - Hyeran Byun, Seong-Whan Lee:
Applications of Support Vector Machines for Pattern Recognition: A Survey. 213-236 - Asanobu Kitamoto:
Typhoon Analysis and Data Mining with Kernel Methods. 237-248
Poster Papers
- Fabrizio Smeraldi, Josef Bigün, Wulfram Gerstner:
Support Vector Features and the Role of Dimensionality in Face Authentication. 249-259 - Yong Ma, Xiaoqing Ding:
Face Detection Based on Cost-Sensitive Support Vector Machines. 260-267 - Seonghoon Kang, Hyeran Byun, Seong-Whan Lee:
Real-Time Pedestrian Detection Using Support Vector Machines. 268-277 - Shantanu Chakrabartty, Gert Cauwenberghs:
Forward Decoding Kernel Machines: A Hybrid HMM/SVM Approach to Sequence Recognition. 278-292 - Kwang In Kim, Keechul Jung, Jin Hyung Kim:
Color Texture-Based Object Detection: An Application to License Plate Localization. 293-309 - Stefan Rüping:
Support Vector Machines in Relational Databases. 310-320 - Zeyu Li, Shiwei Tang, Shuicheng Yan:
Multi-Class SVM Classifier Based on Pairwise Coupling. 321-333 - Jennifer Huang, Volker Blanz, Bernd Heisele:
Face Recognition Using Component-Based SVM Classification and Morphable Models. 334-341 - Jianmin Li, Bo Zhang, Fuzong Lin:
A New Cache Replacement Algorithm in SMO. 342-353 - Nedjem-Eddine Ayat, Mohamed Cheriet, Ching Y. Suen:
Optimization of the SVM Kernels Using an Empirical Error Minimization Scheme. 354-369 - Dihua Xi, Seong-Whan Lee:
Face Detection Based on Support Vector Machines. 370-387 - Björn Johansson, Fredrik Kahl:
Detecting Windows in City Scenes. 388-396 - Hyun-Chul Kim, Shaoning Pang, Hong-Mo Je, Daijin Kim, Sung Yang Bang:
Support Vector Machine Ensemble with Bagging. 397-407 - Eulanda Miranda dos Santos, Herman Martins Gomes:
A Comparative Study of Polynomial Kernel SVM Applied to Appearance-Based Object Recognition. 408-418
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