Evaluating Discrete Bayesian Networks in the Domain of Concept Drift


Evaluating Discrete Bayesian Networks in the Domain of Concept Drift – Kunze (2008) proposes a Bayesian network architecture for action prediction. The architecture is based on a hierarchical learning algorithm and relies heavily on a novel prior function for the model’s posterior distribution. The main innovation here is the use of neural data representation which allows us to handle variable selection and to model a latent variable independently from the model. The proposed architecture allows us to model a state-of-state conditional probability distribution over the state of the underlying latent variable. Thus, a latent state is considered as a posterior distribution over the conditional distributions. This framework is important for many application areas such as: predicting medical images and decision making, or medical images with uncertainty in the input data.

This paper presents the first attempt at an unsupervised machine translation system for English language identification using a deep learning architecture. Specifically, we develop methods based on the Semantic Machine Translation (MMT) framework and propose to learn a deep neural network architecture to train the model to translate a target question into a natural language representation that is automatically represented in a semantic hierarchy of the language.

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Evaluating Discrete Bayesian Networks in the Domain of Concept Drift

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  • Towards Deep Learning Models for Electronic Health Records: A Comprehensive and Interpretable Study

    Complexity and Generalization: An Ensemble Approach for Text Classification, Retelling and Semantic UnderstandingThis paper presents the first attempt at an unsupervised machine translation system for English language identification using a deep learning architecture. Specifically, we develop methods based on the Semantic Machine Translation (MMT) framework and propose to learn a deep neural network architecture to train the model to translate a target question into a natural language representation that is automatically represented in a semantic hierarchy of the language.


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