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We introduce an embedded method that simultaneously selects relevant features during classifier construction by penalizing each feature's use in the dual formulation of support vector machines (SVM). This approach called kernel-penalized SVM (KP-SVM) optimizes the shape of an anisotropic RBF Kernel eliminating features that have low relevance for the(More)
Demand forecasts play a crucial role for supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Several forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivates the development of hybrid systems combining(More)
We propose a linearly penalized support vector machines (LP-SVM) model for feature selection. Its application to a problem of customer retention and a comparison with other feature selection techniques underlines its effectiveness. 1 Introduction One of the tasks of Statistics and Data Mining consists of extracting patterns contained in large data bases. In(More)
When a user visits a web site, important information concerning his/her preferences and behavior is stored implicitly in the associated log files. This information can be revealed by using data mining techniques and can be used in order to improve both, content and structure of the respective web site. From the set of possible that define the visitor's(More)
| W e address the issue of call acceptance and routing in ATM networks. Our goal is to design an algorithm that guarantees bounds on the fraction of cells lost by a call. The method we propose for call acceptance and routing does not require models describing the traac. Each switch estimates the additional fraction of cells that would be lost if new calls(More)