Sentence examples for nonlinear data from inspiring English sources

The RBF SVR method has three model parameters: the already discussed ε and C and the extra kernel function parameter γ, which determines the degree of nonlinearity: setting γ to high values results in a highly nonlinear data model [ 39].

Nonlinear data structures are of varied sorts.

The nonlinear data is nearly superpositioned on the experimental data.

It can classify highly nonlinear data using kernel function.

The accuracy levels attained were 100% in case of linear data and 98% for nonlinear data.

Support vector machine (SVM) is a method for the classification of both linear and nonlinear data.

Hinton and Salakhutdinov [7] demonstrated the nonlinear data dimensionality reduction by use of autoencoder.

Neural networks have also been used in the geophysical context for nonlinear data representation [29].

Therefore, its formulation for the energy-frequency representation of nonlinear data can be misleading[5].

Mixtures of probabilistic principal component analyzers model high-dimensional nonlinear data by combining local linear models.

Thus, for highly nonlinear data distribution, these kernel methods are more suitable for low-resolution and illumination variation conditions.