Machine learning has been successfully applied in many fields such as facial recognition, voice recognition, and image processing. Machine learning is a process used to train computers to distinguish between objects of different types, or classes, through exposure to examples of objects of various types.
A key step in the process is the selection and determination of which “features” of the objects will be used in learning the differences between the classes. The result of the learning process is called a classifier. Classifiers allow computers to predict the class of an object they have never seen before and can therefore be used to search for objects of similar type. In many fields, this automated method of search and discovery has replaced slower, manual methods.
Consider the application of machine learning to finding the picture of a particular person among many images in an electronic album, a task called facial recognition. If the album is relatively small a human could look at every picture and determine if the person of interest was present.
If, however, the album consisted of vast numbers of images, an automated search and discovery process is helpful in reducing the number of images that a human needs to examine. The images in the reduced set are those the search and discovery process determined contained the person of interest with a high degree of certainty.
Not all images will actually contain the person of interest, but due to the high accuracy of the machine learning algorithms there is a tremendous reduction in the amount of time needed to investigate the entire album. This technique is successfully used today by law enforcement and public safety agencies for finding suspect terrorists, criminals, and missing persons.
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