Tom M. Mitchell provided a widely quoted definition: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
Generalization is the ability of a machine learning algorithm to perform accurately on new, unseen examples after training on a finite data set. The core objective of a learner is to generalize from its experience. The training examples from its experience come from some generally unknown probability distribution and the learner has to extract from them something more general, something about that distribution, that allows it to produce useful answers in new cases.
Machine learning, knowledge discovery in databases (KDD) and data mining
These three terms are commonly confused, as they often employ the same methods and overlap strongly. They can be roughly separated as follows:
Machine learning focuses on the prediction, based on known properties learned from the training data
Data mining (which is the analysis step of Knowledge Discovery in Databases) focuses on the discovery of (previously) unknown properties on the data
However, these two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, the performance is usually evaluated with respect to the ability to reproduce known knowledge, while in KDD the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed... [continues]
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