Today, most of the data in business applications is stored in relational database systems or in data warehouses built on top of relational database systems. Often, for more data is available than can be processed by standard learning algorithms in reasonable time. This paper presents an...

Support Vector Machines (SVMs) have become a popular tool for learning with large amounts of high dimensional data. However, it may sometimes be preferable to learn incrementally from previousSVM results, as computing a SVM is very costly in terms of time and memory consumption or because the...

In this paper, we consider the projected gradient algorithms for solving the quadratic program with bound constraints and a single linear equality constraint (SLBQP). We establish the relationship between the Lagrangian multiplier in the projection subproblem and the Lagrangian multiplier in the...