An Adaptive Meta-Model for Large-Scale, Real-World Data Interpretation

An Adaptive Meta-Model for Large-Scale, Real-World Data Interpretation – This paper addresses the problem of automatically inferring a class, from data or a database, of a group of people from the given data. As we can not see a direct relationship between a set of people and a subset of data, the problem is to infer a class (i.e., group of people) of a subset of data. Our formulation can improve the prediction performance of several automatic methods for this problem. The first approach, inspired by the concept of a generative model, takes the data in a multivariate framework which provides a generative model. The model is then used to learn information on individuals or groups of individuals, by using an discriminator mechanism based on a set of labels for a set of groups of individuals. We show that this model can achieve an expected improvement in accuracy over several approaches.

We present an unsupervised and efficient learning framework for supervised learning from deep CNNs. Deep neural networks perform well under a variety of settings, including the supervised setting, with an improvement of 17% in the classification accuracy for the best dataset used. The method has been evaluated on the task of predicting large-scale pedestrian detection. Experimental results show that the proposed approach improves classification accuracy by 27% in classification accuracy with increasing learning rate.

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An Adaptive Meta-Model for Large-Scale, Real-World Data Interpretation

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    Training with Improved Deep CNNs Requires to Deepize for Effective ClassificationWe present an unsupervised and efficient learning framework for supervised learning from deep CNNs. Deep neural networks perform well under a variety of settings, including the supervised setting, with an improvement of 17% in the classification accuracy for the best dataset used. The method has been evaluated on the task of predicting large-scale pedestrian detection. Experimental results show that the proposed approach improves classification accuracy by 27% in classification accuracy with increasing learning rate.


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