Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models.
Commonly-used learning methods include neural networks, Bayesian classifiers, nearest-neighbor methods, and so on; here, we use SVMs.
There are many models discussed in the literature for prediction of the product properties however, nonlinear modeling methods including artificial neural networks (ANNs) and fuzzy models have shown better performance.
Nowadays, Artificial Intelligence (AI) methods including Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been proved as a novel and potential tools with acceptable accuracy for modeling of engineering systems.
To this end, three intelligence methods including artificial neural network (ANN), support vector machine (SVM) and least square support vector machine (LSSVM) have been applied to forecast the N2O solubility in 25 ILs.
We integrate these correlated protein features into an accurate alignment potential using advanced statistical learning methods, including conditional neural fields (Peng et al., 2009).
To automate this step, a variety of methods have been proposed, including neural networks [4], singular value decomposition [5,6], wavelet-based smoothing [7], among others.
The Swiss Federal Department of Home Affairs therefore decided in 1998 to add five methods of complementary medicine, including Neural Therapy (NT), to the benefit catalogue of basic health insurance for a trial period of five years.
We employed a combination of methods to facilitate a correlative approach, including neural pathway-tracing, immunohistochemistry, behavioral neuropharmacology, meal entrainment conditioning, and excitotoxic unilateral lesion-mediated neural 'disconnection' between pharmacological vHP GHSR activation and the dpLHA.
