Learning Bayesian Networks from Data with Unknown Labels: Theories and Experiments


Learning Bayesian Networks from Data with Unknown Labels: Theories and Experiments – This paper describes a technique for learning a probabilistic model for uncertain data. This model predicts some unknowns of an unknown sample. The prediction can be easily computed using a probability measure and also is accurate to be used as a tool for decision makers in a machine learning system. This probabilistic model has been used to classify data from multiple applications, and has been used for decision analysis and to assess the modelability of the model.

This paper addresses the problem of predicting a lexical description for the purpose of providing a lexical characterization of the word. Using the recently proposed notion of the “tendency-preserving” ability of language to preserve both semantic meaning and its interpretability, we propose a novel framework for predicting how well an article about an entity describes that entity. The article describes how the entity is structured in a language and the entity’s behavior is reflected by its lexicon. The framework is able to predict this entity’s behavior and to infer how well a description of that entity relates to the entity’s lexicon. To test the robustness of the framework we use an example from Wikipedia to quantify the semantic similarity of the entities’ lexicon and to measure whether or not the entity’s lexicon is more consistent with its descriptions. We show that our framework is robust to the task of predicting how well an entity describes that entity.

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Learning Bayesian Networks from Data with Unknown Labels: Theories and Experiments

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  • Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks

    Exploring the possibility of the formation of syntactically coherent linguistic units by learning how to readThis paper addresses the problem of predicting a lexical description for the purpose of providing a lexical characterization of the word. Using the recently proposed notion of the “tendency-preserving” ability of language to preserve both semantic meaning and its interpretability, we propose a novel framework for predicting how well an article about an entity describes that entity. The article describes how the entity is structured in a language and the entity’s behavior is reflected by its lexicon. The framework is able to predict this entity’s behavior and to infer how well a description of that entity relates to the entity’s lexicon. To test the robustness of the framework we use an example from Wikipedia to quantify the semantic similarity of the entities’ lexicon and to measure whether or not the entity’s lexicon is more consistent with its descriptions. We show that our framework is robust to the task of predicting how well an entity describes that entity.


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