Sentence examples for for large training from inspiring English sources

Learning is much faster especially for large training sets.

GPR is robust and easy to implement but becomes inefficient for large training sets compared to NN and MARS.

These two files need to be read into memory by Bioclipse and for large training sets these files may be large.

However, training a contextual additive structure using algorithms proposed in [19, 20] is still computationally expensive for large training databases (more than 500 sentences).

On the other hand, for large training databases, larger number of contextual regions has to be defined to escape from under-fitting model to training data.

First, since sparse-coding-only approach is a lazy learning, the optimization can be slow for large training set.

The potentially large number of selected factors again increases the need for larger training corpora, which may not be available for some languages or applications, causing data sparsity problems.

The linear SVM in LIBLINEAR produces models with similar predictive performance (for larger training set sizes) as the RBF SVM approach in πSVM, but with dramatically lower modelling time.

Our method is compared with another single-genome method (TripletSVM) in two datasets, showing better performance in one and comparable performance in the other, for larger training sets.

However, our method outperforms a machine learning approach based on a SVM in one dataset and has comparable performance with the other, for larger training sets.

For larger training sets LOOCV becomes computationally expensive and so a 5-Fold cross-validation (5CV) is used on all training sets with more than 100 genes (12 TFs fit this criteria).