Sentence examples for simultaneous learning from inspiring English sources

Because the whole self-learning process includes simultaneous learning among multiple regions, the time required to complete self-learning within a certain range (denoted as ( {D}_{mathrm{LEA}}^{mathrm{TOT}} )) is the maximum value of the self-learning time between the regions.

To overcome this problem, a simultaneous learning framework for clustering and classification (SCC) is presented in this paper.

The main idea of this paper is to optimize two objective functions which represent fuzzy cluster connectedness and classification error rate to achieve the goal of simultaneous learning.

We then analyzed the combination of these two algorithms in an efficient sdyna instance for simultaneous learning and planning using mdds.

Research shows that the simultaneous learning of language and content requires teachers' (a) understanding of how language works in content, (b) planning for it across a unit, and (c) supporting students' linguistic engagement in classroom activities (Schleppegrell and O'Hallaron 2011).

To alleviate these limitations, the authors in [19] proposed a MSHMM structure that allows for simultaneous learning of all model parameters, including the stream relevance weights, by linearizing the approximation of the pdf.

In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solving prowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions.

Finally, the very nature of educational interventions involves multiple simultaneous sequential learning methods and experiences.

This work supports a shift in myoelectric control schemes towards proportional simultaneous controls learned through development of unique muscle synergies.

Moreover, Li et al. propose a deep pairwise-supervised hashing to firstly perform simultaneous feature learning and hash-code learning for applications with pairwise labels [71].

This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems.