The Particle Swarm Optimization (PSO) and Support Vector Machines (SVMs) approaches are used for predicting the thermodynamic parameters for the 1:1 inclusion complexation of chiral guests with beta-cyclodextrin. A PSO is adopted for descriptor selection in the quantitative structure-property relationships (QSPR) of a dataset of 74 chiral guests due to its simplicity, speed, and consistency. Th...

The non-parametric deterministic Support Vector Machines (SVMs) produce high levels of performances in text classification. This article offers a much needed evaluation of the Gaussian Process (GP) classifier, as a non-parametric probabilistic analogue to SVMs, which has been rarely applied to text classification. We provide an extensive experimental comparison of the performance and properties...

The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks, which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in...

Support Vector Machines have acquired a central position in the field of Machine Learning and Pattern Recognition in the past decade and have been known to deliver state-of-theart performance in applications such as text categorization, hand-written character recognition, bio-sequence analysis, etc. In this article we provide a gentle introduction into the workings of Support Vector Machines (a...

This paper introduces Transductive Support Vector Machines (TSVMs) for text classi cation. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimize misclassi cations of just those particular examples. The paper presents an analysis of why TSVMs are ...

The aim of this work is to examine the feasibilities of the support vector machines (SVMs) and K-nearest neighbor (K-NN) classifier methods for the classification of an aquifer in the Khuzestan Province, Iran. For this purpose, 17 groundwater quality variables including EC, TDS, turbidity, pH, total hardness, Ca, Mg, total alkalinity, sulfate, nitrate, nitrite, fluoride, phosphate, Fe, Mn, Cu, ...

This paper presents an efficient mode decision scheme for down-sizing video transcoding in H.264 using support vector machines (SVMs). In order to reduce the high computational complexity of using conventional mode decision in the H.264 re-encoder, the proposed scheme uses SVMs to exploit the correlation between coding information extracted from the input high-resolution bit-stream and the codi...

Support vector machines (SVMs) proved to be highly efficient computational tools in various classification tasks. However, the knowledge learned by an SVM is encoded in a long list of parameter values, and it is not easy to comprehend what the SVM is actually computing. We show that certain types of SVMs are mathematically equivalent to a specific fuzzy–rule base, the fuzzy all–permutations rul...

In this paper, we proposed a new method of applying Support Vector Machines (SVMs) for cancer classification. We proposed a hybrid classifier that considers the degree of a membership function of each class with the help of Fuzzy Naive Bayes (FNB) and then organizes one-versus-rest (OVR) SVMs as the architecture classifying into the corresponding class. In this method, we used a novel system of...

Traditionally, statistical techniques such as multivariate discriminant analysis and logistic regression analysis have been applied for predicting financial distresses by analyzing financial ratios. In addition to statistical methods, recent studies suggest that backpropagation neural networks (BPNs) and support vector machines (SVMs) can be alternative approaches for classification tasks. Henc...