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A similar model to the successful neurological commissioning support service, where commissioners can benefit from the experience of people who use neurology services, needs to be developed for learning disabilities.
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Over the last decade, several methods have been developed for learning the probabilities from a database.
Recently, a new approach has been developed for learning the prior based on sparse representations.
In this paper, a novel Mandarin edutainment system is developed for learning Mandarin in immersing, interactive virtual learning environments (VLE).
Then, a variational Bayesian expectation maximization (VBEM) algorithm is developed for learning parameters of the robust linear dynamic system.
An effective shrinkage estimation procedure was developed for learning the VAR models from small sample data [1].
Many machine learning approaches have been developed for learning tree ensembles (for review, see [ 41]), including Bagging [ 42] and Boosting [ 43].
In the past several years, approaches in probabilistic graphical models (PGMs) have been developed for learning causal networks compatible with multiple levels of observations.
Structure Expectation-Maximization (SEM) has been developed for learning Probabilistic network structure from data with hidden variables and missing values [ 13].
To do this, a standardized test framework could be developed for heterogeneous transfer learning algorithms, as was done for homogeneous transfer learning algorithms [111, 112].
The specialized forebrain nuclei of songbirds are developed for vocal learning.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com