Sentence examples for learning with multiple from inspiring English sources

It is, therefore, crucial for carefully selecting a good strategy for learning with multiple labels.

The framework draws on research on professional design processes and on learning with multiple external representations.

This article contributes to a deeper understanding of learning with multiple resources, widely available due to digitalisation.

The learning module consisted of a series of interactive seminars and discussions utilising blended learning with multiple teaching approaches including lectures, online multimedia study, role-playing, and activities.

The DeFT (Design, Functions, Tasks) framework for learning with multiple representations integrates research on learning, the cognitive science of representation and constructivist theories of education.

Central implications are that learning with multiple resources places high demands on the learners' participation, and the need to account for the moment-by-moment actions in a long-term, shared process, spread across participants, materials and contexts.

Eye-tracking indicated that participants who learned with single frames paid more attention to the important representations than participants who learned with multiple frames.

Deep learning methods are representation learning methods with multiple levels of representation, obtained by composing simply but nonlinear modules that each transforms the representation at one level (starting with the raw input) into a higher representation slightly more abstract level, with the composition of enough such transformations, and very complex functions can be learned [1, 2].

A case is studied to validate the efficiency of the proposed parameter learning approach with multiple referenced values.

In this paper, we incorporate the dynamic pairwise constraints into MEKL to propose a novel Multiple Empirical Kernel Learning with dynamic Pairwise Constraints method (MEKLPC).

This study proposes LS-Q learning architecture that can deal with general reinforcement learning tasks with multiple states and delayed reward.