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Behavioural Brain Research, 2010 'Learning to learn' phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated-a process termed 'learning to learn'
Carsten Mehring +2 more
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On Learning Decision Structures
Fundamenta Informaticae, 1997A decision structure is a simple and powerful tool for organizing a decision process. It differs from a conventional decision tree in that its nodes are assigned tests that can be functions of the attributes, rather than single attributes; the branches stemming from a node can be assigned a subset of attribute values rather than a single attribute ...
Imam, Ibrahim F., Michalski, Ryszard S.
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Structural learning and the hippocampus
Hippocampus, 2007AbstractIt is argued that while the hippocampus is not vital for all classes of configural learning, it is vital for a specific subclass of configural tasks called “structural learning.” The defining feature of structural learning is that in addition to binding stimulus elements to make unique arrays (as in all configural learning), the relationship of
John P, Aggleton +2 more
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Mesochronal Structure Learning
Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence, 2018Standard time series structure learning algorithms assume that the measurement timescale is approximately the same as the timescale of the underlying (causal) system. In many scientific contexts, however, this assumption is violated: the measurement timescale can be substantially slower than the system timescale (so intermediate time series datapoints ...
Sergey M. Plis, David Danks, Jianyu Yang
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Learning structured representations
Neurocomputing, 2003Abstract SHRUTI is a connectionist model that demonstrates how a network of neuron-like elements can encode a large body of semantic, episodic, and causal knowledge, and rapidly make decisions and perform explanatory and predictive reasoning. To further ground this model in the functioning of the brain it must be shown that components of the model ...
Lokendra Shastri, Carter Wendelken
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The Cost of Structure Learning
Journal of Cognitive Neuroscience, 2017Abstract Human learning is highly efficient and flexible. A key contributor to this learning flexibility is our ability to generalize new information across contexts that we know require the same behavior and to transfer rules to new contexts we encounter.
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2016
We study the problem of learning ensembles in the online setting, when the hypotheses are selected out of a base family that may be a union of possibly very complex sub-families. We prove new theoretical guarantees for the online learning of such ensembles in terms of the sequential Rademacher complexities of these sub-families.
Mehryar Mohri, Scott Yang
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We study the problem of learning ensembles in the online setting, when the hypotheses are selected out of a base family that may be a union of possibly very complex sub-families. We prove new theoretical guarantees for the online learning of such ensembles in terms of the sequential Rademacher complexities of these sub-families.
Mehryar Mohri, Scott Yang
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Structural learning with forgetting
Neural Networks, 1996Abstract It is widely known that, despite its popularity, back propagation learning suffers from various difficulties. There have been many studies aiming at the solution of these. Among them there are a class of learning algorithms, which I call structural learning, aiming at small-sized networks requiring less computational cost.
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2022
This report documents the program and the outcomes of Dagstuhl Seminar 21362 "Structure and Learning", held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 ...
Dong, Tiansi +4 more
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This report documents the program and the outcomes of Dagstuhl Seminar 21362 "Structure and Learning", held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 ...
Dong, Tiansi +4 more
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Self-aware and Learning Structure
2006This study focuses on learning of control commands identification and load identification for active shape control of a tensegrity structure in situations of unknown loading event. Control commands are defined as sequences of contractions and elongations of active struts. Case-based reasoning strategies support learning. Simple retrieval and adaptation
Adam, B., Smith, I.F.C.
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