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Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors.
Reinforcement learning (RL) is a widely used learning paradigm for adaptive agents.
Effect of MAM vs saline treatment on GD17 on performance in the reversal learning paradigm of the Morris' water maze.
Reinforcement learning (RL) is a biologically supported learning paradigm, which allows an agent to learn through experience acquired by interaction with its environment.
The ensemble machine learning paradigm has been established to tackle this challenge.
To answer this question a bilateral transfer of learning paradigm was used.
In this study, we use a miniature artificial language learning paradigm to provide direct experimental evidence for this explanatory hypothesis.
This demonstrates the significance of the hybrid machine learning paradigm in solving petroleum engineering problems with improved accuracies.
Following the supervised machine learning paradigm, the subset is divided into training and testing parts.
The task-irrelevant perceptual learning paradigm [27], [29] was used for this study (see Figure 1).
To facilitate these extensions, the machine learning paradigm we most closely follow is supervised or reinforcement learning (RL) [35].
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