acute lymphoblastic leukemia is the most common form of pediatric cancer which is categorized into three l1, l2, and l3 and could bedetected through screening of blood and bone marrow smears by pathologists. due to being time‑consuming and tediousness of theprocedure, a computer‑based system is acquired for convenient detection of acute lymphoblastic leukemia. microscopic images areacquired fro...

Steel surface defect detection is essentially one of pattern recognition problems. Support Vector Machines (SVMs) are known as one of the most proper classifiers in this application. In this paper, we introduce a more accurate classification method by using SVMs as our final classifier of the inspection system. In this scheme, multiclass classification task is performed based on the ”one-agains...

In this paper, we make a proposal to build a classifier with fuzzy outputs with multiclass Support Vector Machines (SVMs). This allows us to widen the applicability of this kind of powerful and soundly founded classifiers. We consider the pairwise multiclass version and investigate different alternatives for the aggregation process. A number of experiments have been carried out to establish the...

This article proposes the multiclass proximal support vector machine (MPSVM) classifier, which extends the binary PSVM to the multiclass case. Unlike the one-versus-rest approach that constructs the decision rule based on multiple binary classification tasks, the proposed method considers all classes simultaneously and has better theoretical properties and empirical performance. We formulate th...

The support vector machines were originally created to classify binary problems. Their extension for multiclass problems was the subject of several researches. Usually, a multiclass classifier is obtained by combining several binary classifiers. During the last years, the attention is focused on four main models of Multi-class Support Vector Machines (M-SVM), which consider all classes simultan...

in this paper a novel fuzzy scheme for medical x-ray image classification is presented. in this method, any image is partitioned in to 25 overlapping subimages and then shape-texture features are extracted from shape and directional information extracted from any subimage. in the classification stage, we apply a fuzzy membership to any subimage with respect to euclidean distance between feature...