Making large-scale support vector machine learning practical
Advances in kernel methods
Data preparation for data mining
Data preparation for data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach
International Journal of Bioinformatics Research and Applications
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Today, most of the data in business applications is stored in relational databases. Relational database systems are so popular, because they offer solutions to many problems around data storage, such as efficiency, effectiveness, usability, security and multi-user support. To benefit from these advantages in Support Vector Machine (SVM) learning, we will develop an SVM implementation that can be run inside a relational database system. Even if this kind of implementation obviously cannot be as efficient as a standalone implementation, it will be favorable in situations, where requirements other than efficiency for learning play an important role.