On the other hand, while both f-learners and TD-learners showed robust TD-related activation in the striatum and OFC, TD-learners showed significantly stronger TD -related activation than f-learners in the OFC (Fig. 4d; Supporting Information Tables S11 and S12).
f-learners showed robust f +-related responses in the striatum, OFC and AIC, while these neural responses were absent in TD-learners at the same threshold (Fig. 4c; Supporting Information Table S9).
The second layer of phishGILLNET (phishGILLNET2) employs classifier ensemble technique AdaBoost and topic probabilities as features to build a robust classifier using several base learners.
The experimental results revealed that the combination of the FCBF feature selection and ICA-based RotBoost ensemble with several base learners is a robust method for microarray classification.
Instruction that engages both auditory and visual channels, for example, presenting plant images simultaneously with text or narration, provides more opportunities for the learner to engage with botanical instruction, allows the learner to build robust mental models, and supports the integration of those models with prior knowledge, thus leading to meaningful learning about plants.
But for users looking to gain practical language skills, linguistics research has shown evidence that creating a robust vocabulary can give a learner comprehension of about 70 to 80percentt of the language.
The impacts of noise, however, vary dramatically depending on the learning algorithm and simple algorithms such as naïve Bayes and nearest neighbor learners are often more robust than more complex learners such as support vector machines or random forests.
Vocational theory suggests that learners cannot form worthwhile, robust vocational identities without experiencing work and without actively engaging and learning in workplaces.
That is, the base learners utilized in a robust ensemble classifier should be of high classification accuracy and avoid making coincident misclassification errors which in turn necessitate the diverse learners.
Typical e-learning trust interactions between e-learners and providers are presented, demonstrating that robust security mechanisms and effective trust control can be obtained and implemented.
