Currently, neural networks deliver state of the art performance on multiple machine learning tasks, mainly because of their ability to learn features.
The emphasis here is not on investigating a preferably extensive set of problems as in algorithm selection; it is mostly used for a smaller selection of multiple related learning tasks.
MIDA consists of two versions, i.e., binary-class MIDA (B-MIDA) and multi-class MIDA (M-MIDA), which are utilized to cope with binary-class (standard) and multi-class multiple-instance learning tasks, respectively.
Behavioral analyses found that GluR2-cKO mice were impaired on multiple hippocampus-dependent learning tasks that required NMDAR activation.
Moulin et al. (2004), using a 10-word multiple-trial learning task, found that both AD and MCI patients showed lower GA and greater LA of items between trials relative to normal controls [ 12].
This study proposes LS-Q learning architecture that can deal with general reinforcement learning tasks with multiple states and delayed reward.
A change in cognition, defined by the same cognitive and learning tasks on multiple occasions, may also be explained by the ease at which children memorize and engage in habitual action (Kember et al., 2000).
To improve performance in learning tasks involving multiple trials, an individual must carry out two processes.
