Book ChapterDOI
A new multi-class support vector machine with multi-sphere in the feature space
Pei-Yi Hao,Yen-Hsiu Lin +1 more
- pp 756-765
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TLDR
Experimental results show that the proposed method for extending the SVM method of pattern recognition for solving the multi-class problem in one formal step is more suitable for practical use than other multi- class SVMs, especially for unbalanced datasets.Abstract:
Support vector machine (SVM) is a very promising classification technique developed by Vapnik. However, there are still some shortcomings in the original SVM approach. First, SVM was originally designed for binary classification. How to extend it effectively for multiclass classification is still an on-going research issue. Second, SVM does not consider the distribution of each class. In this paper, we propose an extension to the SVM method of pattern recognition for solving the multi-class problem in one formal step. Contrast to previous multi-class SVMs, our approach considers the distribution of each class. Experimental results show that the proposed method is more suitable for practical use than other multi-class SVMs, especially for unbalanced datasets.read more
Citations
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Journal ArticleDOI
Diagnosis for PEMFC Systems: A Data-Driven Approach With the Capabilities of Online Adaptation and Novel Fault Detection
TL;DR: In this paper, a data-driven strategy is proposed for polymer electrolyte membrane fuel cell system diagnosis and a classification method named spherical-shaped multiple-class support vector machine is used to classify the extracted features into various classes related to health states.
Journal ArticleDOI
Fault diagnosis for fuel cell systems: A data-driven approach using high-precise voltage sensors
TL;DR: This study proposes the criteria used for evaluating a diagnosis strategy and experimentally demonstrates an online fault diagnosis strategy designed for Proton Exchange Membrane Fuel Cell (PEMFC) systems, promising to be utilized in various fuel cell systems and promote the commercialization of fuel cell technology.
Proceedings Article
Deep multi-sphere support vector data description
TL;DR: The proposed Deep Multi-sphere Support Vector Data Description, which jointly optimises the objectives of the deep network and anomaly detection, generates useful and discriminative features by embeding normal data with a multi-modal distribution into multiple data-enclosing hyper-spheres with minimum volume.
Journal ArticleDOI
Fault Diagnosis for PEMFC Systems in Consideration of Dynamic Behaviors and Spatial Inhomogeneity
TL;DR: The individual cell voltages measured in a sliding diagnosis window are considered integrally as a diagnostic observation and a time-series analysis tool, named shapelet transform, is used to extract the discriminative features from the diagnostic observations.
Journal ArticleDOI
Diagnosis for PEMFC Based on Magnetic Measurements and Data-Driven Approach
TL;DR: A quantitative data-driven diagnosis strategy based on the magnetic measurement for fault diagnosis and an index is proposed to quantify the faulty level as a fault is diagnosed to show its pros and cons.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Proceedings ArticleDOI
Advances in kernel methods: support vector learning
TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
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