Learning Theory

By Ding-Xuan Zhou, Qiangwu, Yiming Ying. 2001

Objective: To offer an alternative perspective on current continuing education practices and to propose a model for facilitating continuing education in the athletic training workplace. Background: Professional knowledge can quickly become outdated, and the personaVprofessional contexts of allied medical fields such as athletic training are becoming increasingly more complex, making continuing education paramount. Adult learning theory suggests that individuals are selfdirected, autonomous learners in nature and that experience is a rich source for leaming, subsequently making the workplace a fruitful environment in which to engage in continuing education. Unfortunately, mandating continuing education may violate the voluntary nature of adult learning, making the practice questionable. Therefore, alternative aspects of continuing education may be helpful.

Subfields: Scaling & Learning Theory

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “2016), as well as models of causal reasoning and learning built on the theory of causal Bayesian networks (Goodman et al. 2011; Griffiths & Tenenbaum 2005, 2009).”
Meta-Learning for Semi-Supervised Few-Shot Classification (2018, background): “Our algorithm is also related to transductive learning (Vapnik, 1998; Joachims, 1999; Fu et al., 2015), where the base classiﬁer gets reﬁned by seeing the unlabeled examples.”
A PAC-Bayesian bound for Lifelong Learning (2013, background): “As such, the bound resembled the typical trade-off in regularized risk minimization between the training loss and a regularizer (Vapnik, 1998).”
The placebo effect: dissolving the expectancy versus conditioning debate. (2004, unknown): “First, as would be predicted from the general finding that a stronger US produces a stronger CR, rats given two doses of cyclophosphamide during the conditioning stage later exhibited greater conditioned immunosuppression than those given only one dose.”
A General Framework for Counterfactual Learning-to-Rank (2018, background): “Under the standard uniform convergence conditions [32], the unbi-asedness of the risk estimate implies consistency in the sense that given enough training data, the learning algorithm is guaranteed to ind the best system in S .”
Classifiers in almost empty spaces (2000, methodology): “• Other, nonlinear kernels as discussed in section 2, may be used as well (provided that they fulfill Mercer’s theorem [5]).”
Mercer kernel-based clustering in feature space (2002, methodology): “This follows on from the Support Vector classification methods which employ Mercer kernel representations of feature space dot-products and the unsupervised method for performing feature space principal component analysis (KPCA) [11], [10].”
An Introduction to Variable and Feature Selection (2003, unknown): “Many other types of penalization of the training error have been proposed in the literature (see, e.g., Vapnik, 1998, Hastie et al., 2001).”
Awareness, different learning conditions, and second language development (2004, methodology): “In cognitive psychology, feedback has traditionally been linked to the process of hypothesis formation and testing (Estes, 1989), a process found in SLA studies investigating different levels of awareness (e.g., Leow, 1997, 2001; Rosa & O’Neill, 1999).”
Capacity of reproducing kernel spaces in learning theory (2003, background): “This is of particular interest when is finite dimensional, which includes the important example with in [20].”
Content-based audio classification and retrieval by support vector machines (2003, methodology): “We refer to [22] for more details on the nonseparable case.”
Avoiding versus seeking: the relationship of information seeking to avoidance, blunting, coping, dissonance, and related concepts. (2005, background): “According to Bandura [48, 61], individuals’ sense of locus of control affects their feelings of self-efficacy.”
An assessment of fixed-capacity models of visual working memory (2008, background): “These guessing rates indicate a biased and probabilistic response strategy when items were out of memory and are approximately concordant with previous probability matching results (23, 24).”