The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
Response modeling with support vector machines
Expert Systems with Applications: An International Journal
Constructing response model using ensemble based on feature subset selection
Expert Systems with Applications: An International Journal
ϵ-Tube based pattern selection for support vector machines
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Quantifying the indirect effects of a marketing contact
Expert Systems with Applications: An International Journal
Flood disaster loss comprehensive evaluation model based on optimization support vector machine
Expert Systems with Applications: An International Journal
Accounting for the long-term effects of a marketing contact
Expert Systems with Applications: An International Journal
Bootstrap based pattern selection for support vector regression
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Data augmentation by predicting spending pleasure using commercially available external data
Journal of Intelligent Information Systems
Pattern selection for support vector regression based response modeling
Expert Systems with Applications: An International Journal
Including spatial interdependence in customer acquisition models: A cross-category comparison
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
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Response modeling has become a key factor to direct marketing. In general, there are two stages in response modeling. The first stage is to identify respondents from a customer database while the second stage is to estimate purchase amounts of the respondents. This paper focuses on the second stage where a regression, not a classification, problem is solved. Recently, several non-linear models based on machine learning such as support vector machines (SVM) have been applied to response modeling. However, there is a major difficulty. A typical training dataset for response modeling is so large that modeling takes very long, or, even worse, modeling may be impossible. Therefore, sampling methods have been usually employed in practice. However a sampled dataset usually leads to lower accuracy. In this paper, we employed an @e-tube based sampling for support vector regression (SVR) which leads to better accuracy than the random sampling method.