Text-mining methods have become a key feature for homeland-security technologies, as they can help explore effectively increasing masses of digital documents in the search for relevant information. This research presents a model for document clustering that arranges unstructured documents into content-based homogeneous groups. The overall paradigm is hybrid because it combines pattern-recogniti...

As the support vector (SV) number of a support vector machine (SVM) determines the execution speed of the testing phase, there have been diverse methods to reduce it. Although iterative preimage addition (IPA), belonging to the ‘reduced set construction’, is reported to be able to reduce a large portion of the SV number of a standard SVM when the kernel is a radial basis function (RBF), the fac...

For high-dimensional data classification such as hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted featur...

Face Recognition has crucial effects in daily life especially for security purposes and their tasks are actively being used for many applications. In this study, we introduce a hybrid face recognition technique, consisting of two main parts namely feature extraction and classification. In the first part, as feature extracting techniques, we benefit from Eigenfaces method which is based on Princ...

The impact of reliable estimation of stream flows at highly urbanized areas and the associated receiving waters is very important for water resources analysis and design. We used the least squares support vector machine (LS-SVM) based algorithm to forecast the future streamflow discharge. A Gaussian Radial Basis Function (RBF) kernel framework was built on the data set to optimize the tuning pa...

Based on statistical learning theory (SLT), the support vector machine (SVM) is well recognized as a powerful computational tool for problems with nonlinearity having high dimensionalities. Solving the problem of feature and kernel parameter selection is a difficult task in machine learning and of high practical relevance in blurred fault diagnosis. We explored the feasibility of applying an ar...

In this paper, we investigate an approach for classification of mammographic masses as benign or malign. This study relies on a combination of Support Vector Machine (SVM) and wavelet-based subband image decomposition. Decision making was performed in two stages as feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features....

– While the core quality of SVM comes from its ability to get global optima, classification performance also depends on computing kernels. However, while this kernel-complexity generates power machine, it is responsible for compu- tational load execute kernel. Moreover, insisting a similarity function be positive definite kernel demands some properties satisfied that seem unproductive sometimes...