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Oznake: computer science;machine learning;manifold learning
Intuitively, learning should be easier when the data points lie on a low-dimensional submanifold of the input space. Recently there has been a growing interest in algorithms that aim to exploit such geometrical properties of the data. Oftentimes these algorithms require estimating the dimension of t ...
Leto: 2007 Vir: videolectures.net
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Oznake: computer science;machine learning;reinforcement learning
Many practitioners of reinforcement learning problems have observed that oftentimes the performance of the agent reaches very close to the optimal performance even though the estimated (action-)value function is still far from the optimal one. The goal of this paper is to explain and formalize this ...
Leto: 2011 Vir: videolectures.net
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Oznake: computer science;machine learning;reinforcement learning
We consider the problem of model selection in the batch (offline, non-interactive) reinforcement learning setting when the goal is to find an action-value function with the smallest Bellman error among a countable set of candidate functions. We propose a complexity regularization-based model selecti ...
Leto: 2011 Vir: videolectures.net
Video in druga učna gradiva
Oznake: computer science;machine learning;deep learning;reinforcement learning;unsupervised learning
Leto: 2018 Vir: videolectures.net
Št. zadetkov: 5
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