Towards Large-grained Visual Classification by Optimizing for Hierarchical Feature Learning


Towards Large-grained Visual Classification by Optimizing for Hierarchical Feature Learning – We propose a new dataset in which subjects were asked to describe the visual content of images, and to identify whether they would prefer this content or another image. A simple image-object segmentation method, named ‘Deep Categorization’ was used to predict visual content according to the subjects’ visual content. We show a new dataset with up to 888K labeled subjects to study the effects of image-object segmentation and classify subjects via both ‘object category’ and ‘image category’ of the dataset. We also show the importance of the subjects’ visual interests for our dataset, showing that visual categories are an informative model for categorization.

The Bayesian network has the opportunity of having a fundamental role in many problems in finance. With the interest of finance, there has been a large effort in applying Bayesian networks in the financial sector. In this paper, we describe a new algorithm for Bayesian networks, called Pareto-Bayesian networks (PBN). The method is presented as a special case of a general class of PBNs. Based on the previous work, we show that the PBN can be efficiently trained as a Markov decision process or an ensemble of networks. A first example is given. It is shown that if two PBNs are combined together, they can be successfully identified from a database using a novel algorithm. The results of our experiments on real financial applications show that the PBN can be the primary tool in solving practical risk-theoretic decision-making task.

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Towards Large-grained Visual Classification by Optimizing for Hierarchical Feature Learning

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  • Learning to Improve Vector Quantization for Scalable Image Recognition

    Mixture of DAG-causal patterns and conditional probability in trainable Bayesian networksThe Bayesian network has the opportunity of having a fundamental role in many problems in finance. With the interest of finance, there has been a large effort in applying Bayesian networks in the financial sector. In this paper, we describe a new algorithm for Bayesian networks, called Pareto-Bayesian networks (PBN). The method is presented as a special case of a general class of PBNs. Based on the previous work, we show that the PBN can be efficiently trained as a Markov decision process or an ensemble of networks. A first example is given. It is shown that if two PBNs are combined together, they can be successfully identified from a database using a novel algorithm. The results of our experiments on real financial applications show that the PBN can be the primary tool in solving practical risk-theoretic decision-making task.


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