Abstract: An implementation of SVM functionality improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A system for support vector machine processing comprises data stored in the system, a client application programming interface operable to provide an interface to client software, a build unit operable to build a support vector machine model on at least a portion of the data stored in the system, the portion of the data selected using a stratified sampling method with respect to a target distribution, an apply unit operable to apply the support vector machine model using the data stored in the system.

Abstract: An implementation of SVM functionality improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A computer program product for support vector machine processing in a computer system comprises computer program instructions for storing data, providing an interface to client software, building a support vector machine model on at least a portion of the stored data, based on a plurality of model-building parameters, estimating values for at least some of the model-building parameters, and applying the support vector machine model using the stored data to generate a data mining output.

Abstract: An implementation of NMF functionality integrated into a relational database management system provides the capability to apply NMF to relational datasets and to sparse datasets. A database management system comprises a multi-dimensional data table operable to store data and a processing unit operable to perform non-negative matrix factorization on data stored in the multi-dimensional data table and to generate a plurality of data tables, each data table being smaller than the multi-dimensional data table and having reduced dimensionality relative to the multi-dimensional data table. The multi-dimensional data table may be a relational data table.

Abstract: An implementation of SVM functionality integrated into a relational database management system (RDBMS) improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A database management system comprises data stored in the database management system and a processing unit comprising a client application programming interface operable to provide an interface to client software, a build unit operable to build a support vector machine model on at least a portion of the data stored in the database management system, and an apply unit operable to apply the support vector machine model using the data stored in the database management system. The database management system may be a relational database management system.

Abstract: An implementation of SVM functionality improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A system for support vector machine processing comprises data stored in the system, a client application programming interface operable to provide an interface to client software, a build unit operable to build a support vector machine model on at least a portion of the data stored in the system, the portion of the data selected using a stratified sampling method with respect to a target distribution, an apply unit operable to apply the support vector machine model using the data stored in the system.

Abstract: An implementation of SVM functionality improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A computer program product for support vector machine processing in a computer system comprises computer program instructions for storing data, providing an interface to client software, building a support vector machine model on at least a portion of the stored data, based on a plurality of model-building parameters, estimating values for at least some of the model-building parameters, and applying the support vector machine model using the stored data to generate a data mining output.

Abstract: An implementation of SVM functionality improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A system for support vector machine processing comprises data stored in the system, a client application programming interface operable to provide an interface to client software, a build unit operable to build a support vector machine model on at least a portion of the data stored in the system, based on a plurality of model-building parameters, a parameter estimation unit operable to estimate values for at least some of the model-building parameters, and an apply unit operable to apply the support vector machine model using the data stored in the system.

Abstract: A method, system, and computer program product for generating an Adaptive Bayes Network data mining model includes receiving a data table having a plurality of predictor columns and a target column, constructing a plurality of single-predictor models, ranking each single-predictor model using minimum description length and selecting a best single predictor model, performing feature selection, constructing a Naïve Bayes model, comparing a description length of the Naive Bayes model with a description length of a baseline model, replacing the baseline model with the Naïve Bayes model, if the description length of the Naive Bayes model is less than the description length of the baseline model, extending a plurality of single-predictor models in rank order, stepwise, to multi-predictor features, and testing whether each new feature should be included in or should replace a current model state using minimum description length.