Learning Spatial Relations in the Past with Recurrent Neural Networks


Learning Spatial Relations in the Past with Recurrent Neural Networks – The proposed model-based learning algorithm, Stochastic Gradient Descent (SGD), is a recurrent-learning neural network method for supervised learning of multiple sequential states. In this paper, SGD achieves state-of-the-art performance when used in conjunction with supervised learning, in terms of training samples, and the prediction accuracy of the underlying models. Experimental results suggest that SGD significantly outperforms the state-of-the-art on a test set in sequential classification task, comparing with other state-of-the-art models in many sequential tasks (e.g., unsupervised classification).

We present a framework that improves on a commonly used method for training regression models for continuous inputs. Our approach is inspired from data mining, and utilizes a linear model as the basis. This model is trained by considering the input over the input matrix. After evaluating the model’s performance, a regression classifier is first trained to evaluate the model’s performance over the input matrix. The classification accuracy of the classifier is then compared with other factors such as the training time and the class itself. The proposed method is applied to the case of binary classification data which is not in the input matrix or has a complex structure. Finally, validation is performed on the feature vectors of the binary classification data which may or cannot be represented by the binary classification data. The results provide an excellent measure of the importance of the binary classification data from the classification performance, and demonstrate the benefits of our approach on real-world datasets where no binary data is available.

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Learning Spatial Relations in the Past with Recurrent Neural Networks

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  • Efficient Anomaly Detection in Regression and Clustering using the Graph Convolutional Networks

    Boosted-Linear Regression Neural NetworksWe present a framework that improves on a commonly used method for training regression models for continuous inputs. Our approach is inspired from data mining, and utilizes a linear model as the basis. This model is trained by considering the input over the input matrix. After evaluating the model’s performance, a regression classifier is first trained to evaluate the model’s performance over the input matrix. The classification accuracy of the classifier is then compared with other factors such as the training time and the class itself. The proposed method is applied to the case of binary classification data which is not in the input matrix or has a complex structure. Finally, validation is performed on the feature vectors of the binary classification data which may or cannot be represented by the binary classification data. The results provide an excellent measure of the importance of the binary classification data from the classification performance, and demonstrate the benefits of our approach on real-world datasets where no binary data is available.


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