Adaptive Nonlinear Weighted Sparse Coding with Asymmetric Neighborhood Matching for Latent Topic Models


Adaptive Nonlinear Weighted Sparse Coding with Asymmetric Neighborhood Matching for Latent Topic Models – The current proposal combines the well-known semantic-text matching technique of Laplaceau (1984). It is based on combining the similarity and the mutual information between a set of semantic texts, which is an important feature of the common representations of words in various natural language applications. We present three different semantic classes that could be used to obtain a set of text semantic information. As a first contribution, one set of text information is considered as the matching class, the other two as the matching class, and a final pair of matching class is considered as the semantic class, which is a semi-supervised model. We used a multi-instance supervised learning technique to extract the semantic class, and then tested our classifier using these multiple instances. Experimental results show that our method outperforms the conventional classifiers in terms of performance in terms of both semantic and text matching.

This paper describes the first neural networks for time-varying brain time series feature classification in neural networks. This work provides further evidences showing that neural networks are much more robust as compared to any other classifiers. We provide several benchmark datasets, including the Neuro-Virals and the Medical Mature (MBM) datasets, to evaluate the effectiveness of various features on time series.

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Adaptive Nonlinear Weighted Sparse Coding with Asymmetric Neighborhood Matching for Latent Topic Models

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  • Distributed Sparse Signal Recovery

    An Analysis of Deep Learning for Time-Varying Brain Time Series Feature ClassificationThis paper describes the first neural networks for time-varying brain time series feature classification in neural networks. This work provides further evidences showing that neural networks are much more robust as compared to any other classifiers. We provide several benchmark datasets, including the Neuro-Virals and the Medical Mature (MBM) datasets, to evaluate the effectiveness of various features on time series.


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