Inception-based Modeling of the Influence of Context on Outlier Detection


Inception-based Modeling of the Influence of Context on Outlier Detection – This paper is a summary of all the work done by K. Piyush and A. S. Dutt.

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.

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Inception-based Modeling of the Influence of Context on Outlier Detection

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  • Cascade Backpropagation for Weakly Supervised Object Detection

    Robust Multi-feature Text Detection Using the k-means ClusteringWe 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.


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