Robust Multi-sensor Classification in Partially Parameterised Time-Series Data


Robust Multi-sensor Classification in Partially Parameterised Time-Series Data – Deep learning is the powerful approach that aims at extracting features from the data automatically. This paper presents a new deep neural network (NN) based method that can be easily automated, compared to the existing neural networks based method. NNN learning is a very common approach, as it enables researchers to leverage the features from the data without needing to perform deep convolutional neural network (DNN) training. In this paper, we have shown that Deep learning can be easily automated. First, we propose a novel method for training deep networks, with only very limited training data to be released of. Second, we develop a novel convolutional neural network (CNN) to learn the features from non-linear data. The CNN is designed to learn a sparse sparse representation of the data, and use it to train CNNs. Finally, we propose a new deep network to learn the features from non-linear data. We train CNNs using a new algorithm that extracts features from non-linear data and perform CNNs based on that representation. The results are reported on a real dataset, showing the efficacy of our method.

The goal of this paper is to propose a new algorithm to improve the quality of a graph for solving complex problems such as learning graphs. In particular, we propose a new strategy for solving graphs based on learning-based nonlinearities to increase the prediction accuracy of a graph. The main objective of this paper is to extend the state-of-the-art graph learning algorithm by learning graph edges from a data point. The algorithm is based on a recursive programming approach that exploits the notion of graph edges to obtain a finite set of edges in a graph and then use this finite set to improve the prediction based on the information contained in the graph. Experimental evaluation on five real-world data sets shows that our approach improves the performance of the graph learning algorithm from 0.67 to 0.69 on F1 score, outperforming state-of-the-art graph learning algorithms in terms of accuracy and classification accuracy of F1 classification.

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Robust Multi-sensor Classification in Partially Parameterised Time-Series Data

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    Using an Extended Greedy Algorithm to Improve Prediction and Estimation of Non-Smooth Graph ParametersThe goal of this paper is to propose a new algorithm to improve the quality of a graph for solving complex problems such as learning graphs. In particular, we propose a new strategy for solving graphs based on learning-based nonlinearities to increase the prediction accuracy of a graph. The main objective of this paper is to extend the state-of-the-art graph learning algorithm by learning graph edges from a data point. The algorithm is based on a recursive programming approach that exploits the notion of graph edges to obtain a finite set of edges in a graph and then use this finite set to improve the prediction based on the information contained in the graph. Experimental evaluation on five real-world data sets shows that our approach improves the performance of the graph learning algorithm from 0.67 to 0.69 on F1 score, outperforming state-of-the-art graph learning algorithms in terms of accuracy and classification accuracy of F1 classification.


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