Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

Phishing is an act to get someone's important information in the form of usernames, passwords, and other sensitive by providing fake websites that are similar original. (fishing for information) a criminal intends obtain confidential from someone, such as passwords credit cards, impersonating trusted person or business official electronic communication, mail instant messages. Along with develop...

the use of third-party logistics (3pl) providers is regarded as new strategy in logistics management. the relationships by considering 3pl are sometimes more complicated than any classical logistics supplier relationships. these relationships have taken into account as a well-known way to highlight organizations' flexibilities to regard rapidly uncertain market conditions, follow core competenc...

speaker verification is the process of accepting or rejecting claimed identity in terms of its sound features. a speaker verification system can be used for numerous security systems, including bank account accessing, getting to security points, criminology and etc. when a speaker verification system wants to check the identity of individuals remotely, it confronts problems such as noise effect...

in instance-based learning, a training set is given to a classifier for classifying new instances. in practice, not all information in the training set is useful for classifiers. therefore, it is convenient to discard irrelevant instances from the training set. this process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...

This paper focuses on the feature selection in classification via a new version of support vector machine (SVM) named p-norm support vector machine (0 < p < 1). Different from the 2-norm in the standard linear SVM, the p-norm of the normal vector of the decision plane is used which leads to more sparse solution. By using the successive linear algorithm, we can get an approximate local optimal s...