These approaches use learning machine techniques, such as Bayesian and neural networks.
Our results confirm previous ones in the field, simple regression techniques with transformations could perform as well as nonlinear techniques, and ensembles of learning machines techniques such as SMO, M5P or M5R could optimize effort predictions.
In particular, this short survey covers such machining techniques as high-speed machining/cutting (HSM/HSC), hard part machining, dry and near-dry machining (MQL), high-performance machining (HPM), multitasking/complete machining, and assisted and hybrid machining.
Commonly, machine-learning techniques such as support vector machines and random forests [ 74] can be trained on a reserved portion of the dataset and then tested on the remainder.
This method is based on quantitative matrix (QM) and machine learning techniques such as Support Vector Machine (SVM) and Artificial Neural Network (ANN).
They are treated implicitly and translated into other formats such as matrices so that machine learning techniques such as support vector machines (SVM) [21] can be used to predict activities or properties.
Machine learning techniques such as support vector machines (SVM) [ 70] largely function as a black box.
Machine learning techniques, such as support vector machines and self-organising maps, and new features, especially those associated with structural properties of the DNA molecule, have led to progressive improvements in accuracy.
In particular, machine learning techniques such as support vector machines [ 16] and neural networks [ 17] have been applied to analyze transcriptional phenomena associated with disease progression, as well as with the prediction of patient prognoses and clinical response to therapy [ 18- 21].
