Sentence examples for training-based methods from inspiring English sources

While energy-based methods use physical, statistical, or empirical energy functions to estimate the stability change from the protein's three-dimensional structure [ 2- 9], training-based methods are trained on the experimental data from the ProTherm database [ 10] employing machine learning algorithms [ 11- 26].

Finally, the proposed training-based methods, and in particular the DFHMM scheme, can be considered as a computationally efficient alternative to the inference of time-series modeled by a large number of states.

Yet, apart from being computationally tedious, training-based methods depend largely on the test data, and unless a very lengthy set of training data is used, their performance may not be reliable.

These computational approaches can be categorised as energy-based and training-based methods.

Interestingly, a number of the training-based methods allow for a prediction knowing only the sequence of a protein [ 17- 26].

In future works, we plan to research the methods to increase the matching accuracy and combine multiple classifiers using a training-based method.

This subsection proposes a training-based method for channel estimation of multiple AF-relays-assisted cooperative diversity systems in the simple bandwidth-efficient two-hop protocol.

In order to choose a proper peak model, we developed a training-based method to automatically select the best peak model from a set of predefined peak models.

Then, we trained the training-based benchmarks using the equalized training set.

Currently, the methods combining parametric and nonparametric model achieve state-of-the-art results in image and video de-noising, such as the BM3D in [8] algorithm and the training-based sparse de-noising method in [9].

Comparatively, the proposed ST-based channel estimation approach, without entailing any additional bandwidth or constraint, outperforms the FDM training-based estimator [14] by using a small pilot power of Furthermore, the iterative method developed in [24] can be directly employed herein to further improve the estimation performance of our algorithm.