Toward Accurate Text Recognition via Transfer Learning


Toward Accurate Text Recognition via Transfer Learning – We present a new method for text mining that utilizes a combination of multiple semantic and syntactic distance measures to train an intelligent algorithm that is able to extract and recognize the semantic, syntactic and non-syntactic information from a corpus. We evaluate our approach using several datasets and compare the performance of the proposed method. We show that our method performs better than state-of-the-art word segmentation approaches, and that it achieves the best accuracy for recognizing semantic and syntactic information in a corpus.

We propose a novel novel non-negative matrix factorization algorithm based on sparse representation of a vector space. Our method outperforms the state-of-the-art in terms of solving the optimization problem by a significant margin. We present a comprehensive comparison between different approaches and demonstrate an improvement in the prediction performance for the supervised classification problem of MML.

The authors propose a new method of Convolutional Neural Networks (CNN), which is inspired by the traditional sequential optimization for the multi-subject image classification problem. We propose to use a supervised learning method called Gaussian PDEs (GNNs) to map the image regions to the training set of the CNN method. The neural networks are designed for a particular purpose of the image classification problem. The proposed CNN method is based on the GNN’s feature vector representation, the feature representation of the multi-subject image classification problem, and its optimization task. The GNN model has to represent the data in a sparse space using a Gaussian process prior. This work is also motivated by the data augmentation problem, which is an important data augmentation problem where a large number of images undergo multiple augmentation to obtain a higher classification performance. Experimental results show that the proposed method outperforms the state-of-the-art method, while having a negligible performance degradation of accuracy.

Universal Dependency-Aware Knowledge Base Completion

Composite and Complexity of Fuzzy Modeling and Computation

Toward Accurate Text Recognition via Transfer Learning

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  • Sparse Clustering with Missing Data via the Adiabatic Greedy Mixture Model

    A Random Fourier Transform Approach to Compression for Multi-Subject Clinical Image ClassificationThe authors propose a new method of Convolutional Neural Networks (CNN), which is inspired by the traditional sequential optimization for the multi-subject image classification problem. We propose to use a supervised learning method called Gaussian PDEs (GNNs) to map the image regions to the training set of the CNN method. The neural networks are designed for a particular purpose of the image classification problem. The proposed CNN method is based on the GNN’s feature vector representation, the feature representation of the multi-subject image classification problem, and its optimization task. The GNN model has to represent the data in a sparse space using a Gaussian process prior. This work is also motivated by the data augmentation problem, which is an important data augmentation problem where a large number of images undergo multiple augmentation to obtain a higher classification performance. Experimental results show that the proposed method outperforms the state-of-the-art method, while having a negligible performance degradation of accuracy.


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