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      Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]

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            Support vector machines for spam categorization.

            We study the use of support vector machines (SVM's) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM's performed best when using binary features. For both data sets, boosting trees and SVM's had acceptable test performance in terms of accuracy and speed. However, SVM's had significantly less training time.
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              Input space versus feature space in kernel-based methods.

              This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In particular, we review what is known about the shape of the image of input space under the feature space map, and how this influences the capacity of SV methods. Following this, we describe how the metric governing the intrinsic geometry of the mapped surface can be computed in terms of the kernel, using the example of the class of inhomogeneous polynomial kernels, which are often used in SV pattern recognition. We then discuss the connection between feature space and input space by dealing with the question of how one can, given some vector in feature space, find a preimage (exact or approximate) in input space. We describe algorithms to tackle this issue, and show their utility in two applications of kernel methods. First, we use it to reduce the computational complexity of SV decision functions; second, we combine it with the Kernel PCA algorithm, thereby constructing a nonlinear statistical denoising technique which is shown to perform well on real-world data.
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                Author and article information

                Journal
                IEEE Computational Intelligence Magazine
                IEEE Comput. Intell. Mag.
                Institute of Electrical and Electronics Engineers (IEEE)
                1556-603X
                May 2014
                May 2014
                : 9
                : 2
                : 48-57
                Article
                10.1109/MCI.2014.2307227
                3bde9c0d-698e-4981-ad8d-86de5b68c6c1
                © 2014
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