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The String Subsequence Kernel in Text Classification with SVMs
| Content Provider | Semantic Scholar |
|---|---|
| Author | Giuffrida, Michael J. |
| Copyright Year | 2012 |
| Abstract | The support vector machine (SVM) is a method of predicting the classification of input data. Given a set of positive and negative training examples of a class, a binary SVM classifier represents the examples in a high-dimensional space and finds the separating hyperplane between the two classes with the highest margin. If the input data is not linearly separable, it can be transformed into a higher-dimensional feature space. For efficient computation, a kernel function may be used to directly compute the inner product between data points in the feature space without explicitly representing the data in the higherdimensional feature space, thus avoiding the high computational costs associated with feature extraction in the higher-dimensional space. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://mgiuffrida.com/ssk/Proposal.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
