Robust Multi-feature Text Detection Using the k-means Clustering


Robust Multi-feature Text Detection Using the k-means Clustering – We present a new method for text classification which is inspired by a state-of-the-art multi-label learning method. We employ a novel multi-label learning method, i.e. learning to classify the content of a text using multiple labels. The objective of our method is to classify the content of a text while avoiding the need to assign labels to each label. We evaluate our approach on the ITC2012 event dataset and show that both classification and ranking performance are substantially improved under the multi-label approach. Further, we apply the method in a real-world text recognition task where the word similarity measure was not accurately measured, which led to improvement over the state of the art approaches.

In this work, we show how to model time-dependent random variables in a stochastic Bayesian network and how they impact the stochastic gradient descent. First, we propose an auxiliary function that can be used to directly measure the relative gradient error. Secondly, we extend the supervised decision block to a multi-level supervised learning model, where the posterior of the stochastic block is updated in the process of learning the stochastic gradient. Our approach addresses two key challenges in stochastic Bayesian networks: 1) stochastic gradient descent and 2) time-observable learning and learning over complex data and complex data. We show how to update the posterior in a supervised manner using the stochastic method as the auxiliary function. Experimental results show that the proposed method significantly improves the state of the art supervised stochastic Bayesian network prediction performance by an incremental number of orders of magnitude over a standard variational regularization-based stochastic gradient descent model.

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Robust Multi-feature Text Detection Using the k-means Clustering

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  • Learning Unsupervised Object Localization for 6-DoF Scene Labeling

    Diving into the unknown: Fast and accurate low-rank regularized stochastic variational inferenceIn this work, we show how to model time-dependent random variables in a stochastic Bayesian network and how they impact the stochastic gradient descent. First, we propose an auxiliary function that can be used to directly measure the relative gradient error. Secondly, we extend the supervised decision block to a multi-level supervised learning model, where the posterior of the stochastic block is updated in the process of learning the stochastic gradient. Our approach addresses two key challenges in stochastic Bayesian networks: 1) stochastic gradient descent and 2) time-observable learning and learning over complex data and complex data. We show how to update the posterior in a supervised manner using the stochastic method as the auxiliary function. Experimental results show that the proposed method significantly improves the state of the art supervised stochastic Bayesian network prediction performance by an incremental number of orders of magnitude over a standard variational regularization-based stochastic gradient descent model.


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