Identifying Events from Multiscale Sequences with a Bagged Entropic Markov Model – While the task of identifying a sequence is still a critical one in a wide variety of domain related applications, a novel approach for identifying sequences is presented. This works within a framework of a single-step temporal chain to generate sequence information and is based on a multinomial logistic regression and a Bayesian optimization framework. The temporal chain is designed to perform multiple-step regression while simultaneously maximizing a single-step objective function. Our method leverages the best available state-of-the-art sequence classification techniques to generate sequence labeling accuracies using multiple-step temporal chain completion. We show that the proposed structure is much more flexible and can be extended to more sophisticated applications. Using the proposed methodology, we demonstrate that our method can consistently obtain the best sequence labeling accuracies.

In the first part of this paper we apply a nonlinear model to a nonlinear distribution, where each variable has a distribution (a linear function), that has an unknown number of states. The nonlinear model may produce some distribution, but it may not produce the entire distribution. We first show that the model is able to produce some distributions as a function of the time-varying variables from the distribution, and then discuss its generalization capability and the applications. It is shown that when the model is able to produce some distributions, it can be used on problems of interest with a small number of variables, such as classification over the population.

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# Identifying Events from Multiscale Sequences with a Bagged Entropic Markov Model

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Automated Algorithm Selection in Categorical Quadratic ProgrammingIn the first part of this paper we apply a nonlinear model to a nonlinear distribution, where each variable has a distribution (a linear function), that has an unknown number of states. The nonlinear model may produce some distribution, but it may not produce the entire distribution. We first show that the model is able to produce some distributions as a function of the time-varying variables from the distribution, and then discuss its generalization capability and the applications. It is shown that when the model is able to produce some distributions, it can be used on problems of interest with a small number of variables, such as classification over the population.