Neural Networks for Activity Recognition in Mobile Social Media


Neural Networks for Activity Recognition in Mobile Social Media – In this paper, we study the problem of finding the most probable state of a set of spatio-temporally coherent entities in the given temporal scene. This task is typically seen as a quadratic process that requires a very large number of distinct features and can be performed in many cases from multiple approaches. However, there are a number of plausible models that are able to cope with this problem. In this paper, we propose a novel nonlinear nonconvex algorithm (n-CNN) based on the structure of entity and entity information and nonconvexity of the output space. The model has the ability to deal with uncertainty and ambiguity in the source data and can be used for generating new entities in the future. The model is able to perform the task efficiently, achieving a higher accuracy rate than the state-of-the-art approaches despite using only a very small collection of entity and entity information. We also present and analyze three nonlinear CNNs (one representing entity information and one representing entity output) and illustrate the performance of our model.

We tackle a major challenge where a data sets are limited to a set of items, which can be categorized and aggregated. In this paper we propose a new method for this problem. The proposed method is motivated by the fact that most data sets are not well partitioned into categories and aggregated (e.g. by a bag of items). In this study we take the perspective that the best partition function given by the data sets is a weighted sum of each category’s weighted sum. Consequently, given a category, a weighted weight function is defined by comparing the weighted sum of each category. Therefore, this study studies the performance of an aggregated weighted sum-sum estimator. We have investigated the performance of this estimator, in contrast to other recent methods that consider the same weight function. We also propose a data set classification algorithm, which can be designed to handle the different weighted weight functions. These new estimators are evaluated on simulated and real data sets that we performed on. The results show that our new estimators are well-suited for various data analysis tasks.

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Neural Networks for Activity Recognition in Mobile Social Media

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  • Sparse Nonparametric MAP Inference

    An Efficient Online Clustering Algorithm with Latent Factor GraphsWe tackle a major challenge where a data sets are limited to a set of items, which can be categorized and aggregated. In this paper we propose a new method for this problem. The proposed method is motivated by the fact that most data sets are not well partitioned into categories and aggregated (e.g. by a bag of items). In this study we take the perspective that the best partition function given by the data sets is a weighted sum of each category’s weighted sum. Consequently, given a category, a weighted weight function is defined by comparing the weighted sum of each category. Therefore, this study studies the performance of an aggregated weighted sum-sum estimator. We have investigated the performance of this estimator, in contrast to other recent methods that consider the same weight function. We also propose a data set classification algorithm, which can be designed to handle the different weighted weight functions. These new estimators are evaluated on simulated and real data sets that we performed on. The results show that our new estimators are well-suited for various data analysis tasks.


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