Sentence examples for function can be learned from inspiring English sources

In this paper, we show how, in the absence of knowledge to define the potential function manually, this function can be learned online in parallel with the actual reinforcement learning process.

Traditionally, this function can be learned as a regression or classification form, similar to the procedure of Quantitatively Structure Activity Relationship (QSAR) study [5].

Recently, DAEs have been shown to be effective in many noise reduction and reverberation suppression applications because higher-level representations and increased flexibility of the feature mapping function can be learned.

Recently, the denoising autoencoder (DAE), one type of DNN, has been shown to be effective in many noise reduction applications because higher-level representations and increased flexibility of the feature mapping function can be learned [30 33].

Denoising autoencoders (DAEs) have been shown to be effective in many noise reduction applications because higher level representations and increased flexibility of the feature mapping function can be learned [42,43].

Finally, we show how these ergonomic cost functions can be learned online, allowing co-robots to quickly adapt to the preferences of an individual human partner.

Finally, the fact that complex functions can be learned and carried out by areas of brain that are innately 'prewired' (if at all) to do quite different sorts of processing indicates that such competences can be and are acquired without any inborn, task-specific guidance.

Deep learning methods are representation learning methods with multiple levels of representation, obtained by composing simply but nonlinear modules that each transforms the representation at one level (starting with the raw input) into a higher representation slightly more abstract level, with the composition of enough such transformations, and very complex functions can be learned [1, 2].

By use of non-linear kernel functions such as a Gaussian kernel, complex and non-linear decision functions can be learned by the SVM.

Due to the plasticity of synapse generation and function, more can be learned from observing synapses in intact circuits in vivo than from fixed, post-mortem samples.

Protein function, regulation, and interactions can be learned from their structure [ 37, 38], which promotes development of novel methods for the prediction of the protein structure.