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From Theoretical Learnability to Statistical Measures of the Learnable
1999The main focus of theoretical models for machine learning is to formally describe what is the meaning of learnable, what is a learning process, or what is the relationship between a learning agent and a teaching one. However, when we prove from a theoretical point of view that a concept is learnable, we have no a priori idea concerning the difficulty ...
Marc Sebban, Gilles Richard
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On the Learnability of Recursive Data
Mathematics of Control, Signals, and Systems, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Learnability can be undecidable
Nature Machine Intelligence, 2019The mathematical foundations of machine learning play a key role in the development of the field. They improve our understanding and provide tools for designing new learning paradigms. The advantages of mathematics, however, sometimes come with a cost. Godel and Cohen showed, in a nutshell, that not everything is provable.
Shai Ben-David +4 more
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The Learnability of Naive Bayes
2000Naive Bayes is an efficient and effective learning algorithm, but previous results show that its representation ability is severely limited since it can only represent certain linearly separable functions in the binary domain. We give necessary and sufficient conditions on linearly separable functions in the binary domain to be learnable by Naive Bayes
Huajie Zhang +2 more
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2000
Recent theories suggest that language acquisition is assisted by the evolution of languages towards forms that are easily learnable. In this paper, we evolve combinatorial languages which can be learned by a recurrent neural network quickly and from relatively few examples. Additionally, we evolve languages for generalization in different "worlds", and
Tonkes, B., Blair, A. D., Wiles, J. H.
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Recent theories suggest that language acquisition is assisted by the evolution of languages towards forms that are easily learnable. In this paper, we evolve combinatorial languages which can be learned by a recurrent neural network quickly and from relatively few examples. Additionally, we evolve languages for generalization in different "worlds", and
Tonkes, B., Blair, A. D., Wiles, J. H.
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Iterative Learning Control for Discrete-Time Systems With Full Learnability
IEEE Transactions on Neural Networks and Learning Systems, 2022Xiaoe Ruan, Yuanshi Zheng
exaly
The Learnability of Diagram Semantics
2002Conveying information through a diagram depends to some extent on how well it is designed as an input to our visual system. Results of previous work by the author (and collaborators) show that diagrams based on a perceptual syntax (Geon diagrams) can improve the legibility of the semantic content in a diagram.
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