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This paper introduces an algorithm based on MLE to learn the structure and parameters of CDHMM (Continuous Density HMM). One of the most cumbersome troubles encountered in applications that incorporates HMM as a model, is guessing the required number of states and the entire structure especially when sources of information is continuous and variable (e.g. speech). In our algorithm, induction steps...
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...
In this paper, Anomaly Detection by Resource Monitoring (Ayaka), a novel lightweight anomaly and fault detection infrastructure, is presented for Information Appliances. Ayaka provides a general monitoring method for detecting anomalies using only resource usage information on systems independent of its domain, target application and programming languages. Ayaka modifies the kernel to detect faults...
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