Machine learning approaches can be categorized to supervised and unsupervised approaches [31].
We review the literature in this growing field, focusing on how infants growing up bilingual use surface acoustic information to separate, categorize and begin to learn their two languages.
Often, the goal is to learn to categorize objects into one of a relatively small set of classes.
The abilities to learn and to categorize are fundamental for cognitive systems, be it animals or machines, and therefore have attracted attention from engineers and psychologists alike.
For example, neural networks have been touted as providing a means for allowing computers to "learn" how to categorize visual fields correctly, even in the absence of specified rules.
It has a link on it to learn how to categorize a wikiHow article that may be helpful for inspiration on what to write about doing.
This suggests that learning to categorize at the lowest level provides a benefit to categorization at higher levels, at least when the stimuli are difficult to learn.
Furthermore, one may question why these four particular combinations should be the optimal way to categorize embodied learning research given the arguments we presented earlier.
Of these messages, 83.3% of full-time and 65.5% of CEP students' emails were categorized as learning experience emails (Defined as a direct response to a discussion question, sharing learning material, asking questions, clarifying misconceptions, agreeing or disagreeing with other students, or highlighting a teaching point).
But our experience with objects can take many different forms: We can be passively exposed to objects, learn to categorize them into different groups, learn unique names or other meaningful information about them, or learn to discriminate between them without ever naming them.
