large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. as contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. in this paper, a methodology...

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...

A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitation. These connections are generalized to cost-sensitive classification, in a...

Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik’s statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often providing improved results compared with other techniques. The SVMs operate within the framework of regularization theory by minimizing an empirical risk in a...

Solving different types of optimization models (including parameters fitting) for support vector machines on largescale training data is often an expensive computational task. This paper proposes a multilevel algorithmic framework that scales efficiently to very large data sets. Instead of solving the whole training set in one optimization process, the support vectors are obtained and gradually...

In this paper, a model of probability fuzzy support vector machines (PFSVMs) based on the consideration both for fuzzy clustering and probability distributions is proposed. In many applications of traditional support vector machines (SVMs), there are over-fitting problems due to the fact that SVM is sensitive to outliers or noises. In order to solve the problem, the fuzzy support vector machine...

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We extend multiclass SVM to multiple prototypes per class. This allows to combine several simple models (e.g. linear models) in a principled way to get much more expressive decision functions. For this framework, we give a compact constrained quadratic formulation and we suggest an ef£cient algorithm for its optimization that guarantees a local minimum of the objective function. An annealed pro...