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Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
Support vector machines (SVMs) are widely-used for classification in machine learning and data mining tasks. However, they traditionally have been applied to small to medium datasets. Recent need to scale up with data size has attracted research attention to develop new methods and implementation for SVM to perform tasks at scale. Distributed SVMs are relatively new and studied recently, but the distributed...
Logistic regression (LR) for classification is the workhorse in industry, where a set of predefined classes is required. The model, however, fails to work in the case where the class labels are not known in advance, a problem we term label-drift classification. Label-drift classification problem naturally occurs in many applications, especially in the context of streaming settings where the incoming...
This paper provides a brief description of our study proposing improvements to the COCOMO models for estimating maintenance size and effort. The proposed size and effort models take into account characteristics of software maintenance that have not been addressed in the current COCOMO models. We found that the proposed models potentially improve the estimation accuracies of software maintenance projects.
The problem of channel estimation for spatially correlated fading multiple-input multiple-output (MIMO) systems is considered. Based on the channel's second order statistic, the minimum mean-square error (MMSE) channel estimator that works with the superimposed training signal is first developed. The problem of designing the optimal superimposed signal is then addressed and solved with an iterative...
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