On the Complexity of Spatio-Temporal Analysis with Application to Active Learning


On the Complexity of Spatio-Temporal Analysis with Application to Active Learning – Real-time information retrieval is not at all simple and involves many complex and costly problems that arise in the modern day. In this paper, we propose a novel machine learning approach for multi-domain retrieval where the task is to recover items, in terms of their semantic information. Such retrieval would be useful for many applications, such as data augmentation, semantic segmentation or annotation of medical image databases. The proposed approach is based on the use of information from the domain to infer relevant features, and a multi-domain learning approach based on deep learning. We have implemented the algorithm with two reinforcement learning techniques to perform the retrieval tasks, namely online and stochastic backpropagation. The algorithm can be evaluated on a dataset containing the data under two different scenarios from the literature: those with two instances which are in the dataset and those with two instances containing the data of the same dimension and therefore different levels of abstraction. We compared our algorithm with the traditional learning algorithms, such as gradient descent, and show that our method converges to the correct solution with a small penalty.

We present a new algorithm, Deep Q-Learning (DB-L), for clustering data. DB-L is a learning-based optimization algorithm that requires to learn and optimize the data-giver’s Q-function in order to achieve a desired clustering result. We build a new architecture for Deep Q-Learning (DB-L) that is trained in the presence of noise or randomness. In its training stage, however, DB-L builds a graph graph, and then makes Q-learning queries to the map of the graph. We use the new Q-learning architecture to learn Q-learning queries from the graph, and to use data from the cluster to infer the clusters that are best suited to the query. We propose a new method to solve the problem under our new architecture and demonstrate its performance in the experiments.

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On the Complexity of Spatio-Temporal Analysis with Application to Active Learning

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  • Predicting the future behavior of non-monotonic trust relationships

    Efficient Representation Learning for ClassificationWe present a new algorithm, Deep Q-Learning (DB-L), for clustering data. DB-L is a learning-based optimization algorithm that requires to learn and optimize the data-giver’s Q-function in order to achieve a desired clustering result. We build a new architecture for Deep Q-Learning (DB-L) that is trained in the presence of noise or randomness. In its training stage, however, DB-L builds a graph graph, and then makes Q-learning queries to the map of the graph. We use the new Q-learning architecture to learn Q-learning queries from the graph, and to use data from the cluster to infer the clusters that are best suited to the query. We propose a new method to solve the problem under our new architecture and demonstrate its performance in the experiments.


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