In this article we systematically use independent validations for the comparison of learning algorithms, in the context of microarray data for disease-free survival of estrogen receptor-positive breast cancer patients.
Construct validation is an ongoing process of learning more about the construct, making new predictions and then testing them.
Furthermore, the ranking of learning algorithms differs, suggesting that algorithms performing best in cross-validation may be suboptimal when evaluated through independent validation.
Finally, we used both C5 and Support Vector Machines together with three variants of leave-one-out cross-validation to learn and validate concepts of protein kinase inhibitor bindings and the influence of information available about the potential binding partners.
Since the lever-pressing study is considered as a typical test for validation of reward learning, the results from the above data provided evidence to our hypothesis that the optogenetic activation of the excitatory neurons expressing CaMKII α in VTA played a role of virtual reward that led to a positive effect on the reward learning processes.
"The value is not in seeing the information but getting the validation of the learning," says Mark Luetzelschwab, a senior vice president at Agilix.
The described method allows not only for an efficient search through a decision space, but also for a corcurrent validation of the learning algorithm performance on a given data.
Validation of prior learning can be seen as a dynamic, developing method that is designated differently in different countries.
In Sweden, validation of prior learning is defined similarly: "Validation refers to a structured assessment and recognition of experience and skills acquired both within and outside the formal education system.
We have demonstrated the design, development and validation of the machine learning driven prognostic system (MLDPS).
