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...

This study evaluated the impact of startup technology innovations and customer relationship management (CRM) performance on participation, value co-creation, consumer purchase behavior (CPB). analytical empirically tested proposed hypotheses using structural equation modeling (SEM) SmartPLS 3 techniques. Moreover, we used a support vector machine (SVM) algorithm to verify model’s accuracy. SVM ...

Support vector machine classification algorithm has been deeply studied in the field of intelligent information processing with its solid theoretical foundation and excellent performance, it widely used real life, such as face recognition, text classification, sentiment analysis, spam filtering, etc. The time complexity classic support is proportional to square data size, this poses a serious c...

Due to the ever-growing threat of malware application, diverse detection mechanism has been developed byresearchers. Malware relates procedure finding on a host device or determining whether particular program ismalicious benign. An instance is an anti-malware designed automatically identify malicious programs from benign prevent damage system.The methodologyusedincorporatedcutting-edge techniq...

Interfacing a kinetic action of a person to an action of a machine system is an important research topic in many application areas. One of the key factors for intimate human-machine interaction is the ability of the control algorithm to detect and classify different user commands with shortest possible latency, thus making a highly correlated link between cause and effect. In our research, we f...

Aiming at the problem of the large training data set leading to amounts of calculation a sparse approximation algorithm of least squares support vector machine are proposed. Firstly using the thought of matrix block, convert the optimization problem of a Least Squares support vector machine into low order symmetric positive definite linear systems. Furthermore, use conjugate gradient algorithm ...