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Hidden Markov models (HMMs) are widely applied to the analysis of time-dependent data sequences, such as nonlinear signal processing, natural language processing, and bioinformatics. Training data in HMMs have two possible formats: a large set of time-dependent sequential data and an infinitely long sequence. The learning process is one of the main concerns in machine learning. For a large set of...
We consider the problem of semi-supervised learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better utilize the information from unlabeled data by formulating them as a three-class (-1,+1, 0) mixture, where class 0 represents the irrelevant data. This distinguishes our work from the traditional SSL problem where unlabeled...
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