23-02-2015, 03:57 PM
i want Fuzzy SVM .m code for classifiying binary classification with feature set 134X102
can help me?
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fuzzy support vector machine matlab code
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i want Fuzzy SVM .m code for classifiying binary classification with feature set 134X102
can help me?
16-03-2017, 10:26 AM
Support Vector Machine (SVM) is a non-linear classifier that is often reported to produce superior classification results in comparison to other methods. The idea behind the method is to align the input data in a non-linear way with a high dimensional space, where the data can be separated linearly, thus providing a great classification (or regression) performance. One of the bottlenecks of the SVM is the large number of support vectors used from the training set to perform classification (regression) tasks. In my code, I use SSE optimization to increase performance.
The classification of documents, with the flourishing of information delivery on the Internet, has become indispensable and is expected to be available through an automatic categorization of text. This article presents a system of categorization of text to solve the problem of categorization of several classes. The system consists of two modules: the processing module and the classification module. In the first module, ICF and Uni are used as indicators to extract relevant terms. While the fuzzy set theory is incorporated into the OAA-SVM in the classification module, we specifically propose an OAA-FSVM classifier to implement a multi-class classification system. The OAA-SVM and OAA-FSVM benefits are evaluated using the macro-average performance index. Also the statistical significance test is examined by the McNemar test. The results of the empirical study show that the proposed OAA-FSVM method has outperformed OAA-SVM in the problem of text categorization of several classes.
In SVM class, I use my 2D SSE optimized vector code for faster computation. The SVMachine class contains the following functions you need to use:
· SVMachine::SVMachine(const wchar_t* fname); ctor
· int SVMachine:
tatus() const; status after ctor (0 upon success and negative in case of errors)
· unsigned int SVMachine::dimension() const; the dimensionality of the SVM
· int SVMachine::classify(const float* x, double& y) const; to classify unknown vector x.
· SVMachine::SVMachine(const wchar_t* fname); ctor
· int SVMachine:
tatus() const; status after ctor (0 upon success and negative in case of errors)· unsigned int SVMachine::dimension() const; the dimensionality of the SVM
· int SVMachine::classify(const float* x, double& y) const; to classify unknown vector x.
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