Detecting and resolving conflicts in multiword e-mail messages


Detecting and resolving conflicts in multiword e-mail messages – We present a novel method of identifying the semantic content of a text using a novel semantic segmentation criterion that has been developed over three decades of existing works. Our key feature is the use of a new nonlinear feature, called the nonconvex feature, that is equivalent to a Gaussian process feature over the vector space. To further improve the performance of our approach we propose a novel approach based on a novel nonlinear parameterizing factor of a multi-dimensional Euclidean distance to a prior distribution of the text as an output. As we show in the paper, our model converges significantly faster than the state of the art techniques, despite not being able to directly scale to small text sizes. We also demonstrate the efficacy of our approach on several different data sets, including four languages.

In this work, we propose a semi-supervised learning algorithm to learn image features based on a knowledge set from video representations. This knowledge is learned automatically from the learned feature vectors by using a combination of discriminative and discriminative filtering, and the algorithm is trained using a supervised model trained jointly with the video model and deep neural network. Our proposed semi-supervised classification algorithm combines two different aspects: (i) model-free features learning and video feature learning; (ii) feature learning and deep neural network learning (DNN) based learning. The proposed model-free classifier learns the semantic object from the visual scene, and the video representation from this semantic object. The algorithm is trained using a supervised model trained jointly with the video model and DNN. Experiments on various datasets demonstrate that our algorithm outperforms the state-of-the-art semi-supervised classifiers both in the learning task and the embedding task.

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Detecting and resolving conflicts in multiword e-mail messages

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  • An Empirical Evaluation of Reinforcement Learning

    Unsupervised Domain Adaptation for Object DetectionIn this work, we propose a semi-supervised learning algorithm to learn image features based on a knowledge set from video representations. This knowledge is learned automatically from the learned feature vectors by using a combination of discriminative and discriminative filtering, and the algorithm is trained using a supervised model trained jointly with the video model and deep neural network. Our proposed semi-supervised classification algorithm combines two different aspects: (i) model-free features learning and video feature learning; (ii) feature learning and deep neural network learning (DNN) based learning. The proposed model-free classifier learns the semantic object from the visual scene, and the video representation from this semantic object. The algorithm is trained using a supervised model trained jointly with the video model and DNN. Experiments on various datasets demonstrate that our algorithm outperforms the state-of-the-art semi-supervised classifiers both in the learning task and the embedding task.


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