Learning Graph-Structured Data with the Weighted Missing Features


Learning Graph-Structured Data with the Weighted Missing Features – This work attempts to tackle the problem of learning machine learning (ML) models for data. The main advantage of ML is that it directly addresses the problem of learning a model without a knowledge of what the model should learn. The problem involves learning models or knowledge bases that are learned by a model and then a discriminative graph projection scheme is formed in order to embed them into a knowledge base that is better suited to the learning objective. In this paper, we first focus on the use of the model knowledge as a proxy in the learning process. We then propose a general method called Learning Graph-Structured Data (LADS) for learning ML models. This method is applicable to any ML-based learning algorithm that learns a graph structure. Our algorithms are built on the notion of graph similarity as the learning objective. We show how a simple method called Graph-Structured Data Learning (GSD) can be used to model such data. The results show that the use of the concept of graph similarity has the beneficial effect of learning models. We also discuss the importance of the model’s structure when learning an ML model.

This paper presents a novel approach for training deep reinforcement learning agents to anticipate the reward of some tasks. We use supervised learning to model actions given rewards and the reward of the agents are not explicitly represented by value functions. As the goal of the proposed model is to predict the reward of the agents, it is often useful to consider rewards that can be inferred from the expected rewards. We propose the use of a novel metric called the Expectation-Maximization (EM) metric to improve the prediction performance, achieving the best expected rewards observed by the EM.

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Learning Graph-Structured Data with the Weighted Missing Features

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    Deep Neural Network Training of Interactive Video Games with Reinforcement LearningThis paper presents a novel approach for training deep reinforcement learning agents to anticipate the reward of some tasks. We use supervised learning to model actions given rewards and the reward of the agents are not explicitly represented by value functions. As the goal of the proposed model is to predict the reward of the agents, it is often useful to consider rewards that can be inferred from the expected rewards. We propose the use of a novel metric called the Expectation-Maximization (EM) metric to improve the prediction performance, achieving the best expected rewards observed by the EM.


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