Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2017; 26th International C Alessandra Lintas,Stefano Rovetta,Alessandro E.P. Confe

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烧瓶

Estimation of the Change of Agents Behavior Strategy Using State-Action Historyeinforcement learning (IRL) is its opposite; given a history of behaviors of an agent, IRL attempts to determine the unknown characteristics, like a reward function, of the agent. Conventional IRL methods usually assume the agent has taken a stationary policy that is optimal in the environment. Howe

Generosity

Generalising the Discriminative Restricted Boltzmann Machinesssuming the .-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. This paper shows that this function can be extended to the

circumvent

Extracting M of N Rules from Restricted Boltzmann Machineshensible, good knowledge representations should be used. So called M of N rules are a compact way of representing knowledge that has a strong intuitive connection to the structure of neural networks. M of N rules have been used in the past in the context of supervised models but not unsupervised mod

皱痕

Generalized Entropy Cost Function in Neural Networkscal applications ranging from business to medical diagnosis and technical problems. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, which has successfully been applied in other fields. T

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Needlework

https://doi.org/10.1007/978-3-319-68612-7artificial intelligence; bio-embedded electronics; classification and regression trees; computational c

啤酒

978-3-319-68611-0Springer International Publishing AG 2017

Commission

骗子

Fernsehen – Internet – Konvergenzssuming the .-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. This paper shows that this function can be extended to the Binomial and .-Bernoulli hidden units.