Multiview point cloud kernels for semisupervised learning [Lecture Notes]

IEEE Signal Processing Magazine(2009)

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摘要
In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHS...
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关键词
Clouds,Kernel,Semisupervised learning,Estimation error,Approximation error,Convergence,Signal processing algorithms,Support vector machines,Hilbert space
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