Huanhuan Chen – Softwares
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Probabilistic Classification Vector Machines
DescriptionPCVMs is a sparse learning algorithm. After analysing relevance vector machines (RVMs) for classification problems and observe that adopting the same prior for different classes may lead to unstable solutions. To tackle this problem, a signed and truncated Gaussian prior is adopted over every weights in PCVMs, where the sign of prior is determined by the class label, i.e. +1 or -1. Also in PCVMs, the kernel parameters can automatically optimistic during the training part.For more details, you may look at this paper.
TutorailPlease download PCVM Matlab codes here.
DescriptionCluster-Based Regularization(ClusterReg) is a semi-supervised classification(SSC) algorithm. SSC learns from cheap unlabeled data and labeled data to predict the labels of test instances. In order to make use of the information from unlabeled data, there should be an assumed relationship between the true class structure and the data distribution. One assumption is that data points clustered together are likely to have the same class label. ClusterReg takes the partition given by a clustering algorithm as a regularization term in the loss function of an SSC classifier.