Evaluating Hyper-parameter Tuning using Random Search in Support Vector Machines for Software Effort Estimation
by Marcelo Jenkins, Leonardo Villalobos-Arias, Christian Quesada-López, Jose Guevara-Coto, Alexandra Martínez

The process of estimating the effort required to develop a software product is known as software effort estimation (SEE). Among these is support vector regression (SVR), a machine learning algorithm which has been successfully used for effort estimation of cross company (CC) data sets.

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