Support vector machines (SVMs) are a supervised classifier successfully applied in a
plethora of real-life applications. However, they suffer from the important shortcomings of …

The discovery of new materials is one of the driving forces to promote the development of
modern society and technology innovation, the traditional materials research mainly …

The emerging machine learning technique called support vector machines is proposed as a
method for performing nonlinear equalization in communication systems. The support vector …

Support vector machine is a classification model which has been widely used in many
nonlinear and high dimensional pattern recognition problems. However, it is inefficient or …

Pattern selection methods have been traditionally developed with a dependency on a
specific classifier. In contrast, this paper presents a method that selects critical patterns …

Support vector machines (SVMs) are powerful classifiers that have high computational
complexity in the training phase, which can limit their applicability to large datasets. An …

Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to
generalize better than conventional machine learning methods employing the traditional …

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct
sequence code division multiple access (DS-CDMA) signals transmitted through multipath …

Training support vector machines (SVMs) for pixel-based feature extraction purposes from
aerial images requires selecting representative pixels (instances) as a training dataset. In …

The support vector machine (SVM) has been spotlighted in the machine learning community
because of its theoretical soundness and practical performance. When applied to a large …