This book constitutes the refereed proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines， SVM 2002， held in Niagara Falls， Canada in August 2002.The 16 revised full papers and 14 poster papers presented together with two invited contributions were carefully reviewed and selected from 57 full paper submissions. The papers presented span the whole range of topics in pattern recognition with support vector machines from computational theories to implementations and applications.
作者簡介
暫缺《模式識別及支持向量機(jī)》作者簡介
圖書目錄
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 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 …… Author Index
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