Classification of hyperspectral data using a classifier ensemble that is based on support vector machines (SVMs) are addressed. First, the hyperspectral data set is decomposed into a few data sources according to the similarity of the spectral bands. Then, each source is processed separately by performing classification based on SVM. Finally, all outputs are used as input for final decision fus...

Magnetic resonance imaging (MRI) provides detailed anatomic information of any part of the body. In this method a hybrid approach for classification of brain tissue in MRI based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) wavelet based texture feature are extracted from normal and tumor region by using HAAR wavelet. These features are given as input to the SVM classifi...

In this paper, a SVM classifier based on S-Transform is presented for power quality disturbances classification. Firstly, seven types of PQ events are created using Matlab simulation. These signals are analyzed to detect and localize PQ events via S-Transform by visual inspection. Then five significant features of the PQ disturbances are extracted from the S-Transform output. Afterwards, PQ dis...

Combining boosting and Support Vector Machine (SVM) is proved to be beneficial, but it is too complex to be feasible. This paper introduces an efficient way to boost SVM. It embraces the idea of active learning to dynamically select “important” samples into training sample set for constructing base classifiers. This method maintains a small training sample set with settled size in order to cont...

The support vector machine (SVM), an assuring new method for the classification, has been widely used in many areas efficiently. However, the online learning issue of SVM is still not addressed satisfactorily since when a new sample arrives to retrain the SVM to adjust the classifier. This may not be feasible for real-time applications due to the expensive computation cost for re-training the S...

In this paper we evaluate the performance of the highest probability SVM nearest neighbor (HP-SVM-NN) classifier, which combines the ideas of the SVM and k-NN classifiers, on the task of spam filtering. To classify a sample, the HP-SVM-NN classifier does the following: for each k in a predefined set {k1, ..., kN} it trains an SVM model on k nearest labeled samples, uses this model to classify t...

Previous machine learning techniques for answer selection in question answering (QA) have required question-answer training pairs. It has been too expensive and labor-intensive, however, to collect these training pairs. This paper presents a novel unsupervised support vector machine (USVM) classifier for answer selection, which is independent of language and does not require hand-tagged trainin...

acute myelogenous leukemia (aml) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blastsin the bone marrow. careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significantdiagnostic methodology for initial aml screening and considered as the first step toward diagnosis. it is time-consuming and dueto the elusive n...

We participated in the Opinion Retrieval Task and the Polarity Subtask. An SVM classifier was used to determine the opinion polarities of documents. We found that the opinion mean average precisions for the runs using the SVM classifier is better than the opinion mean average precisions for the runs produced solely by the TFIDF retrieval model.