We present a new method for the incremental training of multiclass support vector machines that can simultaneously modify each class separating hyperplane and provide computational efficiency for training tasks where the training data collection is sequentially enriched and dynamic adaptation of the classifier is required over time. An auxiliary function has been designed, that incorporates som...

Selection of relevant genes that will give higher accuracy for sample classification (for example, to distinguish cancerous from normal tissues) is a common task in most microarray data studies. An evolutionary method based on generalization error bound theory of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently. The bound theo...

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, ...

objectives in order to increase the classification accuracy of airs, this study introduces a new hybrid system that incorporates a support vector machine into airs for diagnosing tuberculosis. background tuberculosis (tb) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. diagnosis based on cultured specimens is the...

in the last two decade the use of aerial laser scanner (als) or lidar (light detection and ranging) sensor in geomatics engineering and surveying application has augmented significantly . the main reason of the mentioned phenomenon is the reliability and accuracy of the data obtained by lidar sensors. the output of lidar is unclassified 3d point cloud. classification of the lidar point clouds i...

This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier. The central idea is to subdivide the input data set into smaller subsets and classify individual subsets. The individual subset classification is done using support vector machines classifier. Boosting...

Recent progress in the development of techniques to optimize large-scale classification problems has extended the use of multi-class classification. Specifically the use of multi-class classification algorithms when the dataset is to large to fit into limited memory available of most computers. The most prominent algorithms used today solve the multi-class classification problem through an opti...

Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some pat...

UNLABELLED CellClassifier is a tool for classifying single-cell phenotypes in microscope images. It includes several unique and user-friendly features for classification using multiclass support vector machines AVAILABILITY Source code, user manual and SaveObjectSegmentation CellProfiler module available for download at www.cellclassifier.ethz.ch under the GPL license (implemented in Matlab).

In this paper, we propose a new learning method for multi-class support vector machines based on single class SVM learning method. Unlike the methods 1vs1 and 1vsR, used in the literature and mainly based on binary SVM method, our method learns a classifier for each class from only its samples and then uses these classifiers to obtain a multiclass decision model. To enhance the accuracy of our ...