Layered Concept-Learning and Dynamically Variable Bias Management

By L. Rendell, Raj Sheshu, D. Tcheng. 1987

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Citations in the corpus

One-shot Learning with Memory-Augmented Neural Networks (2016, background): “This learning is guided by knowledge accrued more gradually across tasks, which captures the way in which task structure varies across target domains (Giraud-Carrier et al., 2004; Rendell et al., 1987; Thrun, 1998).”
A Model of Inductive Bias Learning (2000, background): “In Rendell, Seshu, and Tcheng (1987) “VBMS” orVariable Bias Management Systemwas introduced as a mechanism for selecting amongst different learning algorithms when tackling a new learning problem.”
On the predictive power of meta-features in OpenML (2017, background): “The first attempt to characterize datasets was done by Rendell et al. (1987).”
Meta-Learning with Memory-Augmented Neural Networks (2016, background): “This learning is guided by knowledge accrued more gradually across tasks, which captures the way in which task structure varies across target domains (Giraud-Carrier et al., 2004; Rendell et al., 1987; Thrun, 1998).”
Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge (1998, background): “An empirical study carried out in a mobile robot domain shows that TC outperforms its unselective counterpart in situations where only a small number of tasks is relevant.”
Lifelong Learning: A Case Study. (1995, methodology): “Following the terminology in [46], we will refer to the problem of learning bias as the meta-level learning problem.”
Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta (1992, background): “Is there an alternative? If bias is what a learner brings to a learning problem, then how could the learner itself generate an appropriate bias? The only way is to generate the bias from previous learning experience (e.g., Rendell, Seshu, & Tcheng 1987 ).”
Discovering Structure in Multiple Learning Tasks: The TC Algorithm (1996, background): “An empirical study carried out in a mobile robot domain shows that TC outperforms its non-selective counterpart in situations where only a small number of tasks is relevant.”