Support vector machine thesis

With the advance of new technologies and investment possibilities, the statistical or machine learning methods, once reserved exclusively to the professional financial institutions, can be also beneficial to the amateur investors. The method of support vector machines as an alternative to the conservative logistic regression models was studied and its performance compared on the real credit data sets. Especially in combination with the non-linear kernel, SVM proved itself as a competitive approach and provided a slight edge on top of the logistic regression model. The cost for this is much higher computational time, which was needed for the finding of the optimal parameters of the kernel function in particular. The process of model development was time consuming, as well.
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Support Vector Data Description

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Conclusion – Support Vector Machines for Credit Scoring

What are Support Vector Machines? It is more preferred for classification but is sometimes very useful for regression as well. Basically, SVM finds a hyper-plane that creates a boundary between the types of data. In 2-dimensional space, this hyper-plane is nothing but a line. Next, find the optimal hyperplane to separate the data. So by this, you must have understood that inherently, SVM can only perform binary classification i.
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Conclusion

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Data domain description concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a dataset can be used to detect novel data or outliers.
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