SPSS (ver.16, http://www.spss.com) was used in variable selection step to select the most relevant descriptors.
A relevance vector machine is used to automatically select the most relevant terms of the model.
In particular, a relevance vector machine is used to automatically select the most relevant terms of the model while retaining an analytical expression for the predictive distribution.
Companies set real-life challenges from which teachers and lecturers can select the most relevant for their students.
It is driven by experts that select the most relevant tests to be performed.
Then, the sequential backward algorithm is applied in order to select the most relevant features.
In this work, we propose three new decision rules in order to select the most relevant latent variables.
A methodology to select the most relevant input variables and find the best achievable performance for a particular number of inputs is presented.
The proposed method combines Taguchi's experimental design method with backward elimination method to select the most relevant variables from a large set of process variables.
The results show that the tools helped the students to generate, evaluate and select the most relevant ideas and to form teams for project execution.
This work presents a multi-objective wrapper, based on genetic algorithms, to select the most relevant set of features for face recognition tasks.
