Active Learning and Sparsity Constraints over Sparse Mixture Terms


Active Learning and Sparsity Constraints over Sparse Mixture Terms – We report the first evaluation of a convolutional neural network on a real-world classification problem arising in the real-world clinical scenario. The task of predicting the clinical outcome of a patient involves a number of tasks (the classification of a subject and the detection of a disease) and the accuracy of each task is usually dependent on the type of the prediction. To improve the overall effectiveness of the system, we propose a novel and flexible feature vector representation of the task-related information, and propose to use it to learn an efficient discriminant analysis for this task. The classification accuracy is evaluated on a set of 4 different real-world data sets. Results show that the proposed method can outperform the state-of-the-art in predicting the presence and severity of disease in the disease-prepared dataset, achieving an optimal classification accuracies of 73% on the data set.

Sparse-time classification (STR) has emerged as a promising tool for automatic vehicle identification. The main drawback of STR is its lack of training data and the difficulty of handling noisy data. In this work we present an innovative approach to the problem using Convolutional Neural Networks. In our model, we first use unsupervised learning as feature representation for image classification: the Convolutional Neural Network (CNN) is trained with an unlabeled image. The CNN learns a binary metric feature embedding representation of its output vectors (e.g., the k-dimensional). Following this representation, the CNN can model the training data by selecting a high-quality subset of the training data. Our method learns the representations and, by using the learned representations, can be used with the standard segmentation and classification algorithms in order to learn the feature representation for the given dataset. We evaluate our method on the challenging TIDA dataset and compare it to the state-of-the-arts.

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Active Learning and Sparsity Constraints over Sparse Mixture Terms

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  • Learning Structural Knowledge Representations for Relation Classification

    Machine Learning Methods for Multi-Step Traffic AcquisitionSparse-time classification (STR) has emerged as a promising tool for automatic vehicle identification. The main drawback of STR is its lack of training data and the difficulty of handling noisy data. In this work we present an innovative approach to the problem using Convolutional Neural Networks. In our model, we first use unsupervised learning as feature representation for image classification: the Convolutional Neural Network (CNN) is trained with an unlabeled image. The CNN learns a binary metric feature embedding representation of its output vectors (e.g., the k-dimensional). Following this representation, the CNN can model the training data by selecting a high-quality subset of the training data. Our method learns the representations and, by using the learned representations, can be used with the standard segmentation and classification algorithms in order to learn the feature representation for the given dataset. We evaluate our method on the challenging TIDA dataset and compare it to the state-of-the-arts.


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