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.

This paper presents a novel, fully principled, method for a classifier based on a Markov chain Monte Carlo (MCMC) algorithm (Fisher and Gelfond, 2010). In contrast to previous methods that require the entire Bayesian network to be sampled, the proposed method requires the MCMC to be sampled uniformly, and the MCMC is a non-negative matrix. The MCMC algorithm, which runs on a single, stochastic model (the matrix), requires a fixed random matrix to represent the input, and the MCMC is modeled based on linear convergence of the posterior. We show that the proposed method outperforms previous methods and are able to generate high accuracy classification results (using only stochastic models, and thus avoiding overfitting), however there are many practical problems when it is not possible to sample a large number of the parameters for learning the classifier. The proposed method can also be used to reduce the sample number to be sampled as well. We evaluate the performance of the proposed method using benchmarks against state-of-the-art results.

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

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    Efficient Graph Classification Using Smooth Regularized Laplacian ConstraintsThis paper presents a novel, fully principled, method for a classifier based on a Markov chain Monte Carlo (MCMC) algorithm (Fisher and Gelfond, 2010). In contrast to previous methods that require the entire Bayesian network to be sampled, the proposed method requires the MCMC to be sampled uniformly, and the MCMC is a non-negative matrix. The MCMC algorithm, which runs on a single, stochastic model (the matrix), requires a fixed random matrix to represent the input, and the MCMC is modeled based on linear convergence of the posterior. We show that the proposed method outperforms previous methods and are able to generate high accuracy classification results (using only stochastic models, and thus avoiding overfitting), however there are many practical problems when it is not possible to sample a large number of the parameters for learning the classifier. The proposed method can also be used to reduce the sample number to be sampled as well. We evaluate the performance of the proposed method using benchmarks against state-of-the-art results.


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