Sentence examples for decision surface from inspiring English sources

The first is an optimum linear separating hyperplane (decision surface) that separates the data patterns.

Alternatively, one may focus on designing a decision surface that separates the classes directly from the training data set, without having to deduce it from the pdfs.

Such learning machines follow the simple, but powerful idea of nonlinearly mapping input data into high-dimensional feature spaces wherein a linear decision surface discriminating different regions is properly designed.

The proposed model is meritorious in that it considers the synergy as well as the individual role of the k hyperplanes in constructing a decision surface and exploits the advances in theory and algorithms and the advent of powerful softwares for MILP for its solution.

Fig. 1 SVM options for decision surface discovery.

A linear decision surface is evangelized by taking this feature space into consideration.

We discuss the problem of robust classification in terms of a family of decision surfaces, the members of which are functions of a set of global variables.

We also introduce linear multi-dimensional decision surfaces using support vector machines and we also discuss the sensitivity of our classification scheme when the OI is not available.

Thus any probability function can be represented and the decision surfaces can be of any shape, i.e. the classifier is not restricted to a linear form.

The chapter also focuses on a particular family of decision surfaces associated with the Bayesian classification for the specific case of Gaussian density functions.

Examples are presented to show that not all problems are well suited to such approaches, and in those cases, it may be preferable to compute decision surfaces directly by means of alternative costs.