Mining the Web for Semantic Information in Knowledge Bases


Mining the Web for Semantic Information in Knowledge Bases – In this paper, one of the most important task of machine learning is the classification of knowledge bases (KBs). The primary contribution of this work lies in the classification of knowledge based on the knowledge base structure of the data. Therefore, in many applications one needs a classifier, and each class could be determined from it. In this paper, we propose to use a multidimensional matrix to class the multidimensional information from the knowledge base structure, which can lead to classification for many scenarios. In this way, we can make use of multidimensional matrix to classify the knowledge bases. Then we use CNN (Decoder-CNN) to learn the information of the multidimensional matrix to predict the classifier which will be used. We present some experiments on the CNN task for classification of knowledge bases in two datasets: Knowledge Base from MNIST dataset and Knowledge Base from Wikipedia dataset. The experiments show that a multidimensional matrix can improve classification accuracy.

Recent advances in deep learning have shown that deep learning can be used to solve complex problems. However, deep learning is a difficult problem whose many challenges have prevented it from being considered as a natural tool. Motivated by the problem, we propose a new model trained deep learning, called Deep Convolutional Neural Network (DCNN), for the task of multi-view face recognition (MSR). This model uses a hierarchical deep neural network architecture that incorporates many layers, while the layers for the face recognition task are different. The first layer is a layered architecture, while the second layer is a recurrent layer. Each layer is able to solve complex face problems, while the layers for MSR tasks are different. In this paper, we describe the proposed multi-stream DCNN for MSR, and analyze its benefits for both MSR and a variety of other problems.

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Mining the Web for Semantic Information in Knowledge Bases

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    Learning Topic Models by Unifying Stochastic Convex Optimization and Nonconvex LearningRecent advances in deep learning have shown that deep learning can be used to solve complex problems. However, deep learning is a difficult problem whose many challenges have prevented it from being considered as a natural tool. Motivated by the problem, we propose a new model trained deep learning, called Deep Convolutional Neural Network (DCNN), for the task of multi-view face recognition (MSR). This model uses a hierarchical deep neural network architecture that incorporates many layers, while the layers for the face recognition task are different. The first layer is a layered architecture, while the second layer is a recurrent layer. Each layer is able to solve complex face problems, while the layers for MSR tasks are different. In this paper, we describe the proposed multi-stream DCNN for MSR, and analyze its benefits for both MSR and a variety of other problems.


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