Reduce the number of support vectors by using clustering techniques

Tran, Quang-Anh; Zhang, Qianli; Li, Xing

doi:10.1109/icmlc.2003.1259678

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…this purpose, we use kmeans clustering method to assign the data of each class to a given number of groups, and then create a new dataset consisting of only the central vectors of each group [8]. The new dataset size is smaller than the original one.…”

Section: Dendogram Based Svm (Dsvm)mentioning

confidence: 99%

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Fast SVMS for Power Quality Data Mining

Manimala¹,

Selvi²,

Ahila³

2009

Int. Jour. Info. Sci. Comp.

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Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the power quality problem. Support Vector Machines (SVMs) have gained wide acceptance because of the high generalization ability for a wide range of classification applications. Although SVMs have shown potential and promising performance in power disturbances classification, they have been limited by speed particularly when the training data set is large. The hyper plane constructed by SVM is dependent on only a portion of the training samples called support vectors that lie close to the decision boundary (hyper plane). Thus, removing any training samples that are not relevant to support vectors might have no effect on building the proper decision function. We propose the use of clustering techniques such as K-mean to find initial clusters that are further altered to identify non-relevant samples in deciding the decision boundary for SVM. This will help to reduce the number of training samples for SVM without degrading the classification result and classification time can be significantly reduced.

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Section: Dendogram Based Svm (Dsvm)mentioning

confidence: 99%

“…The speed depends on the number of support vectors [8]. Power disturbances classification is an online problem and so the computing time should be drastically reduced.…”

Section: Introductionmentioning

confidence: 99%

Fast SVMS for Power Quality Data Mining

Manimala¹,

Selvi²,

Ahila³

2009

Int. Jour. Info. Sci. Comp.

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Automatic diagnosis of pneumonia using backward elimination method based SVM and its hardware implementation

Bodasingi

Balaji

Jammu

2022

Int J Imaging Syst Tech

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In this paper, an efficient automatic diagnosis system for pneumonia classification is developed using extracted textural features obtained from appropriate wavelet transformation. For feature extraction and analysis in the classification of pneumonia, different wavelet families such as Db3.3, Rbio3.3, Rbio3.5, and Rbio 3.7 are explored. The optimum feature extraction for distinguishing pneumonia infected lungs from normal lungs comes from combining the Db3.3 and Rbio3.7 wavelet families. The features extracted from Db3.3 and Rbio3.7 wavelets are analyzed by feeding to different supervised learning classifiers. It is observed that SVM with RBF kernel is attaining maximum accuracy of 97.5% with σ=2 in the classification. The RBF kernel, on the other hand, is hampered by its lengthy testing computation time. This paper introduces a novel backward elimination based SVM (BESVM) in order to reduce computation time. The suggested method's experimental findings demonstrate the trade‐off between classification speed and performance. This was also noticed when targeting a real‐time hardware software codesign FPGA environment. The amount of support vectors is optimized using the BESVM technique, resulting in a 30% reduction in resource utilization and a 590 ns delay while maintaining accuracy. In terms of area, latency, and hardware efficiency, the suggested BESVM‐based hardware design demonstrates its efficacy.

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Minimum Spanning Set Selection in Graph Kernels

Tortorella,

Micheli

2023

Lecture Notes in Computer Science

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Reduce the number of support vectors by using clustering techniques

Cited by 9 publications

References 4 publications

Fast SVMS for Power Quality Data Mining

Fast SVMS for Power Quality Data Mining

Automatic diagnosis of pneumonia using backward elimination method based SVM and its hardware implementation

Minimum Spanning Set Selection in Graph Kernels

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