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Credit risk analysis plays an important role in the financial market. In this paper, discriminative restricted Boltzmann machine (RBM) is used in credit risk classification. RBM is a generative model associated with an undirected graph, which can capture complicated features from observed data, and after introducing discriminative component into RBM, it can be used to train a non-linear classifier...
A completely automated, high-throughput biodosimetry workstation has been developed by the Center for Minimally Invasive Radiation Biodosimetry at Columbia University over the past few years. To process patients’ blood samples safely and reliably presents a significant challenge in the development of this biodosimetry tool. In this paper, automated failure recognition methods of robotic manipulation...
One important problem in machine learning is how to extract knowledge from prior experience, then transfer and apply this knowledge in new learning tasks. To address this problem, transfer learning leverages information from (supervised) learning on related tasks to facilitate the current learning task. Self-taught learning uses information extracted from (unsupervised) learning on related data. In...
Canonical Correlation Analysis (CCA) is a well-known method for feature extraction and dimension reduction. CCA can simultaneously deal with two sets of data. It makes CCA can be used for feature level fusion. But it suffers the Small Sample Size (SSS) problem. In this paper, a new optimization criterion is presented for overcoming the SSS problem. The optimization problem can be solved analytically...
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