Sentence examples for by mapping the input from inspiring English sources

The fisher kernel approach only learns one single RARG model, and realize the detection by mapping the input ARG into the tangent space of the RARG likelihood manifold.

Now, we will utilize the flexibility of fuzzy logic by mapping the input membership functions to the output memberships functions through a set of rules.

The traditional PR-VSM language recognition system works by mapping the input utterances from data space into a high-dimensional feature space : Φ:→ and then building linear machines in the feature space to find a maximal margin separation.

With regard to binary classification, the SVM trains a classifier by mapping the input samples onto a high-dimensional space through kernel functions, and then seeking a separating hyperplane that differentiates the two classes with maximal margin and minimal error.

Support vector regression (SVR, Vapnik [ 23]) uses linear models to implement non-linear regression by mapping the input space to a higher dimensional feature space using kernel functions.

To classify the data, the SVM trains a classifier by mapping the input samples, using a kernel function in most cases, onto a high-dimensional space, and then seeking a separating hyperplane that differentiates the two classes with maximal margin and minimal error.

The separating hyperplane is determined by a) mapping the input space into a higher dimensional feature space through a kernel function, and b) constructing in this feature space two maximal margin hyperplanes [ 16] to separate the mapped data samples in the higher dimensional space.

This is achieved by mapping the defined input and output spaces onto each other using the nonlinear steadystate model of the process.

This is achieved by mapping the defined input and output spaces onto each other using the nonlinear steady-state model of the process.

Support vector machines (SVM) [ 30] are machine learning algorithms which can approximate non-linear functions by mapping the inputs to a high-dimensional feature space using a kernel function and then, solving a linear problem by finding a maximum margin separating hyperplane.

The basic idea of SVR is to find a function f(x) that has at most ε deviation from the actually obtained target values for all the training samples by mapping the training data from the input feature space into a higher dimensional space, and meanwhile has the capability to approximate future values as accurately as possible [29, 30].