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Previous models of adaptively timed learning propose how a spectrum of cells tuned to brief but different delays are combined and modulated by learning to create a population code for controlling goal-oriented behaviors that span hundreds of milliseconds or even seconds.
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These skills are just as important in the world of work as the fact-based learning proposed by Michael Gove.
The UCI standard dataset is a dataset for machine learning proposed by the University of California, Irvine.
PAC learning (probably approximately correct learning) is a framework for mathematical analysis of machine learning proposed by Valiant [32].
Baseline approaches tested include k-nearest neighbors, SVM, metric learning proposed by Davis [23], feature augmentation proposed by Daumé [22], and a cross domain metric learning method proposed by Saenko [100].
Another technique used to adapt I/O access is supervised machine learning, proposed in [24], focusing on automated provisioning of Hadoop jobs.
From the cognitive model of multimedia learning proposed by [Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation.
In 1997, Freund and Schapire [1] supplied the AdaBoost algorithm for realizing the learning framework of boosted trees, which could be derived from the Probably Approximately Correct (PAC) learning proposed by Valiant [2].
The theory of gamified learning proposed by (Landers et al., 2015) provides two specific causal pathways by which gamification can affect learning and a framework for testing these pathways.
Dual-system theory, when applied to choice under uncertainty, has analogies to reflexive versus reflective learning proposed by Daw and colleagues [94] and heuristic versus logical problem solving proposed by Kahneman and Frederick [95].
A prevailing neural circuit model for olfactory discrimination and learning proposes that KCs serve as temporal coincidence detectors for odors paired with inherently meaningful or conditioned reinforcement [13], [46].
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