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.

We present a deep learning-based clustering method based on the Sparse Bayes algorithm. By leveraging the low-rank structure of the image, the proposed method achieves better clustering performance and better statistical performance than the known ones. The proposed method is designed to be able to perform dense-rank clustering as an efficient optimization algorithm, and our main contribution is in the construction of a sparsifying Bayesian network to capture the sparsity of the image in the sparse sparse structure of the data.

Robust Feature Selection with a Low Complexity Loss

Semi-Supervised Learning of Semantic Representations with the Grouping Attention Kernel

Toward Accurate Text Recognition via Transfer Learning

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  • Learning to Make Predictions under a Budget

    Sparse Depth Estimation via Sparse Gaussian Mixture Modeling and Constraint OptimizationWe present a deep learning-based clustering method based on the Sparse Bayes algorithm. By leveraging the low-rank structure of the image, the proposed method achieves better clustering performance and better statistical performance than the known ones. The proposed method is designed to be able to perform dense-rank clustering as an efficient optimization algorithm, and our main contribution is in the construction of a sparsifying Bayesian network to capture the sparsity of the image in the sparse sparse structure of the data.


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