Response modeling with support vector regression

Response modeling with support vector regression

Response modeling, which predicts whether each customer will respond or how much each customer will spend based on the database of customers, becomes a key factor of direct marketing. In previous researches, several classification approaches, include Support Vector Machines (SVM) and Neural Networks (NN), have been applied for response modeling. However, there are two drawbacks of conventional ...

Response modeling with support vector machines

Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in direct marketing, however, one has to deal with the practical difficulties: large training data, class imbalance and scoring from binary S...

Support vector regression with random output variable and probabilistic constraints

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...

Ltcc Interconnect Modeling by Support Vector Regression

In this paper, we introduce a new method: support vector regression (SVR) method to modeling low temperature co-fired ceramic (LTCC) multilayer interconnect. SVR bases on structural risk minimization (SRM) principle, which leads to good generalization ability. A LTCC based stripline-to-stripline interconnect used as example to verify the proposed method. Experiment results show that the develop...

Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA