Structural Correspondence Analysis for Semi-supervised Learning


Structural Correspondence Analysis for Semi-supervised Learning – Most current methods treat a set of discrete observations (e.g., a model and a test) as a collection of observations. Such approaches typically assume that samples are modeled as discrete samples, which may not be the case. In this work we present a new approach for classification experiments based on Bayesian networks, where the classifier is a single distribution over observations. In addition, we present a generalization error measure that enables us to compare the resulting classifiers to a subset of the observed distributions. To the best of our knowledge, our contribution is the first one to analyze data in this manner, outperforming a state-of-the-art classification algorithm in this task.

In this paper, a deep-learning (DL) approach is proposed based on a combination of CNNs and deep learning methods, and is implemented as an active learning paradigm. The DL framework leverages a CNN to learn an image segmentation problem by learning a sparse representation of the image space to represent the shape. A CNN-CNN model is trained on a dataset of image segmentations, and a deep-learning model of the CNN learning the segmentation problem is deployed to improve the performance of the CNNs. Experiments on public datasets demonstrate that the proposed approach achieves state-of-the-art results.

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Structural Correspondence Analysis for Semi-supervised Learning

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  • An Uncertain Event Calculus: An Example in Cognitive Radio

    Neural image segmentation by multi-scale network learning for spatial consistencyIn this paper, a deep-learning (DL) approach is proposed based on a combination of CNNs and deep learning methods, and is implemented as an active learning paradigm. The DL framework leverages a CNN to learn an image segmentation problem by learning a sparse representation of the image space to represent the shape. A CNN-CNN model is trained on a dataset of image segmentations, and a deep-learning model of the CNN learning the segmentation problem is deployed to improve the performance of the CNNs. Experiments on public datasets demonstrate that the proposed approach achieves state-of-the-art results.


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