Machine Learning
Definition
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.[1]
Generalization
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.[2] 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
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.[1]
Generalization
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.[2] 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
References: ^ * Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7, p.2. ^ Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer ISBN 0-387-31073-8. ^ Yoshua Bengio (2009). Learning Deep Architectures for AI. Now Publishers Inc.. p. 1–3. ISBN 9781601982940. ^ Aharon, M, M Elad, and A Bruckstein. 2006. “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation.” Signal Processing, IEEE Transactions on 54 (11): 4311-4322 ^ "BelKor Home Page" research.att.com Further reading Sergios Theodoridis, Konstantinos Koutroumbas (2009) "Pattern Recognition", 4th Edition, Academic Press, ISBN 978-1-59749-272-0. Ethem Alpaydın (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN 0-262-01211-1 Bing Liu (2007), Web Data Mining: Exploring Hyperlinks, Contents and Usage Data Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56-62, 1957. Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1983), Machine Learning: An Artificial Intelligence Approach, Tioga Publishing Company, ISBN 0-935382-05-4. Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1986), Machine Learning: An Artificial Intelligence Approach, Volume II, Morgan Kaufmann, ISBN 0-934613-00-1. Yves Kodratoff, Ryszard S. Michalski (1990), Machine Learning: An Artificial Intelligence Approach, Volume III, Morgan Kaufmann, ISBN 1-55860-119-8. Ryszard S. Michalski, George Tecuci (1994), Machine Learning: A Multistrategy Approach, Volume IV, Morgan Kaufmann, ISBN 1-55860-251-8. Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2. Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3. Huang T.-M., Kecman V., Kopriva I. (2006), Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN 3-540-31681-7. KECMAN Vojislav (2001), Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN 0-262-11255-8. MacKay, D.J.C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN 0-521-64298-1. Sholom Weiss and Casimir Kulikowski (1991). Computer Systems That Learn, Morgan Kaufmann. ISBN 1-55860-065-5. Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 0-387-95284-5. Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 0-471-03003-1.