Concept Learning - Machine Learning Approaches To Concept Learning

Machine Learning Approaches To Concept Learning

This is a budding field due to recent progress in algorithms, computational power, and the expansion of information on the internet. Unlike the situation in Psychology, the problem of concept learning within machine learning is not one of finding the "right" theory of concept learning, but one of finding the most effective method for a given task. As such, there has been a huge proliferation of concept learning theories. In the machine learning literature, this concept learning is more typically called supervised learning or supervised classification, in contrast to unsupervised learning or unsupervised classification, in which the learner is not provided with class labels. In machine learning, algorithms of in Exemplar theory are also known as instance learners or lazy learners.

There are three important roles for machine learning.

1. Data Mining: this is using historical data to improve decisions. An example is looking at medical records and applying it to medical knowledge when making a diagnoses.
2. Software applications that we cannot program by hand: Examples of this are autonomous driving and speech recognition
3. Self-customizing programs: An example of this is a newsreader that learns a readers particular interests and highlights these when the reader visits the site.

Machine learning has an exciting future. Some future advantages include; learning across full mixed-media data, learning across multiple internal databases (including the internet and news feeds), learning by active experimentation, learning decisions rather than predictions, and the possibility of programming languages with learning embedded.