In order to solve these problems, our method combines the two-dimensional (2-D) CNN-based real-time object detector network with spatiotemporal information.
We review some developments on clustering stochastic processes and come with the conclusion that asymptotically consistent clustering algorithms can be obtained when the processes are ergodic and the dissimilarity measure satisfies the triangle inequality.
We conduct cluster analysis on a class of locally asymptotically self-similar stochastic processes, which includes multifractional Brownian motion as a representative.
We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes.
While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses.
In this paper, we compare and analyze clustering methods with missing data in health behavior research.