An efficient framework for fuzzy classifiers


An efficient framework for fuzzy classifiers – We propose a robust approach for fuzzy classifiers with a limited number of instances that learns to reason about (i.e. the classifiers). The proposed approach consists of three steps. First, we consider each instance separately and make the optimal decision for each instance. Second, we consider the probability of each instance to be a fuzzy probability vector, and perform a Bayesian search to identify the most informative fuzzy classifier. Finally, we perform two Bayesian optimization steps, one for each instance. The final optimization step is designed so that the decision is not made when the most informative fuzzy classifier does not exist.

In this paper we propose an object localization approach for automated odometry from an underwater robot. Our approach is based on the estimation of a large-scale dataset of underwater objects and then comparing them to a novel class of objects. One such dataset, IWODCAR, is available on GitHub and is a well-researched set of objects. We also propose two novel methods, named ConvNet and ResNet, that generalize the ConvNet-ResNet method to new situations such as the task of object localization and detection.

This work presents a novel method to obtain a large-scale image for a given image. The method uses a multiresolution convolutional neural network based on a deep recurrent model. After performing a high-level semantic reasoning test that is based on a high-level language model, a deep classification module is trained. To evaluate the model performance, we then use these results as a prior to evaluate the algorithm’s performance. The experimental results show that the proposed method is able to obtain a large-scale dataset for a given image, given by a number of image segmentation tasks.

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An efficient framework for fuzzy classifiers

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  • Towards Estimating the Effects of Content on Sponsored Search Quality

    Visual-Inertial Odometry by Unsupervised Object LocalizationIn this paper we propose an object localization approach for automated odometry from an underwater robot. Our approach is based on the estimation of a large-scale dataset of underwater objects and then comparing them to a novel class of objects. One such dataset, IWODCAR, is available on GitHub and is a well-researched set of objects. We also propose two novel methods, named ConvNet and ResNet, that generalize the ConvNet-ResNet method to new situations such as the task of object localization and detection.

    This work presents a novel method to obtain a large-scale image for a given image. The method uses a multiresolution convolutional neural network based on a deep recurrent model. After performing a high-level semantic reasoning test that is based on a high-level language model, a deep classification module is trained. To evaluate the model performance, we then use these results as a prior to evaluate the algorithm’s performance. The experimental results show that the proposed method is able to obtain a large-scale dataset for a given image, given by a number of image segmentation tasks.


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