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The question of polynomial learn ability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer science and machine learning. However, despite major progress, the general question of polynomial learn ability of Gaussian mixture distributions still remained open. The current work resolves the question of polynomial...
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...
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