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An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
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TLDR
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata is described.Abstract:
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata by experimentation appendix - some tools for probabilistic analysis.read more
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Proceedings Article
Algorithmic Luckiness
TL;DR: In contrast to standard statistical learning theory which studies uniform bounds on the expected error, the authors presented a framework that exploits the specific learning algorithm used. But the main difference to previous approaches lies in the complexity measure; rather than covering all hypotheses in a given hypothesis space, it is only necessary to cover the functions which could have been learned using the fixed learning algorithm.
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A Computational Learning Theory of Active Object Recognition Under Uncertainty
TL;DR: A number of emergent relations between the object detection noise-rate, the scene representation length, the object class complexity, and the representation class complexity are proved, which demonstrate that selective attention is not only necessary due to computational complexity constraints, but it is also necessary as a noise-suppression mechanism and as a mechanism for efficient object class learning.
Peak reduction in decentralised electricity systems : Markets and prices for flexible planning
TL;DR: In this paper, an agent-based model and a stochastic solution were proposed to plan and balance in future electricity systems, which can deal with the problem of price fluctuations by consumers.
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Simulation-based functional test generation for embedded processors
TL;DR: This work proposes a novel functional test generation approach where simulation results are used to guide the generation of additional tests, and avoids the complexity growth issue by converting some modules in a design into simpler and more efficient models.
Journal ArticleDOI
An incremental learning algorithm with confidence estimation for automated identification of NDE signals
TL;DR: The proposed algorithm, named Learn++, takes advantage of synergistic generalization performance of an ensemble of classifiers in which each classifier is trained with a strategically chosen subset of the training databases that subsequently become available.