Browsing by Subject "Computer Operating Systems"

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    Estelle-based test generation tool
    (Elsevier, 1991) Sarikaya, B.; Forghani, B.; Eswara, S.
    A test design tool for functional analysis and test derivation of protocols formally specified using an extended finitestate machine model is presented. The formal description language supported is Estelle. The tool's main components include a compiler, a normalizer, a multiple module transition tour generator and several interactive programs. The tool is based on a static analysis of Estelle called normalization, which is explained in detail with various examples. The normalized specification facilitates graphical displays of the control and data flow in the specification by the interactive tools. Next discussed is test generation, which is based on verifying the control and data flow. First the data flow graph must be decomposed into blocks where each block represents the data flow in a protocol function. From the control graph the tool generates transition tours, and then test sequences are derived from the transition tour to test each function. The performance of the tool on various applications is also included. © 1991.
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    Instance-based regression by partitioning feature projections
    (Springer, 2004) Uysal, İ.; Güvenir, H. A.
    A new instance-based learning method is presented for regression problems with high-dimensional data. As an instance-based approach, the conventional method, KNN, is very popular for classification. Although KNN performs well on classification tasks, it does not perform as well on regression problems. We have developed a new instance-based method, called Regression by Partitioning Feature Projections (RPFP) which is designed to meet the requirement for a lazy method that achieves high levels of accuracy on regression problems. RPFP gives better performance than well-known eager approaches found in machine learning and statistics such as MARS, rule-based regression, and regression tree induction systems. The most important property of RPFP is that it is a projection-based approach that can handle interactions. We show that it outperforms existing eager or lazy approaches on many domains when there are many missing values in the training data.