Stochastic learning of attribute functions


Stochastic learning of attribute functions – The success of large-scale machine learning applications depends crucially on the ability to infer the full representation of input data, which is challenging to do when data are not easily accessible. In this work, we describe a novel reinforcement learning-based approach for learning the full representation of input data, using a modified version of the Markov Decision Process (MDP) mechanism. The Markov Decision Process learns to predict the actions of a given set of inputs, and it can then apply this prediction to the reward function for each input. The model learns that the reward function is more likely to produce more relevant actions if the number of outputs of the reward function increases. These findings demonstrate that the model can generalize to new inputs, and provide new tools for reinforcement learning that are both theoretically sound and practical for large-scale machine learning.

We propose a new framework for efficient learning of Bayesian networks which is based on minimizing the posterior of the network with a fixed amount of information, and has the following properties: (1) it is NP-hard to approximate posterior estimates in the Bayesian space without using Bayes’ theorem for the posterior; (2) the method generalizes well to sparse networks; (3) the model can be used to learn the posterior on a high dimensional subspace on which Bayes’ theorem are embedded; (4) the method allows to adapt to new datasets, without needing an explicit prior. Our approach outperforms the existing methods in the literature by a significant margin.

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Stochastic learning of attribute functions

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    Fast Bayesian Clustering Algorithms using Approximate Logics with ApplicationsWe propose a new framework for efficient learning of Bayesian networks which is based on minimizing the posterior of the network with a fixed amount of information, and has the following properties: (1) it is NP-hard to approximate posterior estimates in the Bayesian space without using Bayes’ theorem for the posterior; (2) the method generalizes well to sparse networks; (3) the model can be used to learn the posterior on a high dimensional subspace on which Bayes’ theorem are embedded; (4) the method allows to adapt to new datasets, without needing an explicit prior. Our approach outperforms the existing methods in the literature by a significant margin.


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