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We study online sequential logistic regression for churn detection in cellular networks when the feature vectors lie in a high dimensional space on a time varying manifold. We escape the curse of dimensionality by tracking the subspace of the underlying manifold using a hierarchical tree structure. We use the projections of the original high dimensional feature space onto the underlying manifold as...
We study online sequential regression with nonlinearity and time varying statistical distribution when the regressors lie in a high dimensional space. We escape the curse of dimensionality by tracking the subspace of the underlying manifold using a hierarchical tree structure. We use the projections of the original high dimensional regressor space onto the underlying manifold as the modified regressor...
We address nonlinear sequential regression in high dimensional settings when the data lies on a time varying manifold. We solve the curse of dimensionality by tracking the subspace of the underlying manifold using a hierarchical tree structure. Therefore, instead of using the original feature vectors as the input, we use the projections of the high dimensional feature space onto the underlying manifold...
We investigate the coordinated path following problem for unicycles. We define the problem in general but focus on the case of two agent systems with circular paths.
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