A new method for the segmentation of algae images using retinex and support vector machine

In this paper, we proposed a new fuzzy support vector machine(called L2–FSVM here), which error part of object is L2–norm.Meanwhile we introduce a new method of generating fuzzy memberships so as to reduce to effects of outliers. The experimental results demonstrate that the L2-FSVM method provides improved ability to reduce to effects of outliers in comparison with traditional SVMs and FSVMs, and claim that L2–FSVM is the best way to solve the binary classiﬁcation in the three methods stated above.

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A New Method to Mechanical Fault Classification with Support Vector Machine

2010 International Conference on Intelligent System Design and Engineering Application ◽

10.1109/isdea.2010.88 ◽

2010 ◽

Author(s):

Laijun Sun ◽

Mingliang Liu ◽

Haibo Qian ◽

Guangzhong Ye

Keyword(s):

Support Vector Machine ◽

New Method ◽

Fault Classification ◽

Support Vector

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A New Method to Mechanical Fault Classification with Support Vector Machine

2011 Second International Conference on Digital Manufacturing & Automation ◽

10.1109/icdma.2011.25 ◽

2011 ◽

Cited By ~ 2

Author(s):

Laijun Sun ◽

Mingliang Liu ◽

Haibo Qian ◽

Changming Qiao

Keyword(s):

Support Vector Machine ◽

New Method ◽

Fault Classification ◽

Support Vector

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New Method Based on Support Vector Machine in Classification for Hyperspectral Data

2008 International Symposium on Computational Intelligence and Design ◽

10.1109/iscid.2008.61 ◽

2008 ◽

Author(s):

Xiangtao Wang ◽

Yan Feng

Keyword(s):

Support Vector Machine ◽

Hyperspectral Data ◽

New Method ◽

Support Vector

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A new method for mispronunciation detection using Support Vector Machine based on Pronunciation Space Models

Speech Communication ◽

10.1016/j.specom.2009.03.004 ◽

2009 ◽

Vol 51 (10) ◽

pp. 896-905 ◽

Cited By ~ 45

Author(s):

Si Wei ◽

Guoping Hu ◽

Yu Hu ◽

Ren-Hua Wang

Keyword(s):

Support Vector Machine ◽

New Method ◽

Support Vector

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Personal Identification Based on Vectorcardiogram Derived from Limb Leads Electrocardiogram

Journal of Applied Mathematics ◽

10.1155/2012/904905 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Jongshill Lee ◽

Youngjoon Chee ◽

Inyoung Kim

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Personal Identification ◽

Experimental Results ◽

New Method ◽

Support Vector ◽

Inverse Transform ◽

Transform Matrix ◽

Electrocardiogram Ecg

We propose a new method for personal identification using the derived vectorcardiogram (dVCG), which is derived from the limb leads electrocardiogram (ECG). The dVCG was calculated from the standard limb leads ECG using the precalculated inverse transform matrix. Twenty-one features were extracted from the dVCG, and some or all of these 21 features were used in support vector machine (SVM) learning and in tests. The classification accuracy was 99.53%, which is similar to the previous dVCG analysis using the standard 12-lead ECG. Our experimental results show that it is possible to identify a person by features extracted from a dVCG derived from limb leads only. Hence, only three electrodes have to be attached to the person to be identified, which can reduce the effort required to connect electrodes and calculate the dVCG.

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