Unlike similar touch pads on hand-held computers or on laptops, which only recognize input from a single point, this surface can process information from multiple points, allowing for more rapid typing.
The company has said that it uses deep learning, a popular machine-learning technique based on training a many-layered network of simulated neurons to recognize input using a large number of training examples.
The HNN is known to be able to recognize input patterns from a set of stored patterns [15, 21].
Taking loose inspiration from neuroscience, an RCN is encoded with assumptions about visual information like edges or curves which it then uses to recognize inputs it hasn't encountered in training.
They are trained to recognize inputs (e.g. peptide sequences) associated with a given output (e.g. binding affinity).
Two broad categories of inputs are recognized: intermediate inputs and primary factors.
The ASR technology accurately recognized speech input despite variations in dialect, environment, and background noise.
That is, the proposal is an exemplar-based model that "recognizes input and generates outputs by analogical evaluation across a lexicon of distinct memory traces of remembered tokens of speech" (Gahl & Yu, 2006: 213; see also Bod, 2009).
The markup to recognize an input XML is a sample of that input; certain fields are replaced with special tags to match variables against text or subtrees within the input XML.
In this paper we propose a novel approach which demonstrates that having an eigenspace encompassing a set of grey-level images, it is possible to recover the eigenspace coefficients and consequently recognize the input images even in the cases when the input images are 2-tone images of the objects in the training set.
Note that for external input nodes, the ID has to contain the 'IN' prefix which helps the software to recognize external input nodes from the remaining network nodes.
