Although these methods automate the task of learning and inference, they also require experience in selecting classification models (for example, a support vector machine), learning parameters, features or the training data that would result in good performance.
We therefore can map the optimization process onto a new space representation that is more suitable to learning parameters.
Then, a variational Bayesian expectation maximization (VBEM) algorithm is developed for learning parameters of the robust linear dynamic system.
In this case we present a generalization method of the learning parameters based on a tree distance model for the medial axis transformations.
We propose a principled approach for learning parameters in Bayesian networks from incomplete datasets, where the examples of a dataset are subject to equivalence constraints.
Because the log partition function of a MRF has variational representation, we can realize the likelihood computation by variational inference (for example, Loopy Belief Propagation) and learning parameters by variational Expectation-Maximization (EM).
The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc.
We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters.
The learning state estimates learning parameters as a function of the drift function.
Likewise, two learning parameters in RG and MRG are encoded into a six-bit gene.
For example, in network construction and adjusting of learning parameters, there are too many human interferences.
