Machine learning

From Wikipedia, the free encyclopedia

For the journal, see Machine Learning (journal).

As a broad subfield of artificial intelligence, machine learning is concerned with the development of algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods extract rules and patterns out of massive data sets.

Some parts of machine learning are closely related to data mining and statistics. Machine learning research is focused on the computational properties of the statistical methods, such as their computational complexity.

Machine learning has a wide spectrum of applications including natural language processing,syntactic pattern recognition, search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.

Contents 1 Human interaction 2 Algorithm types 3 Machine learning topics 4 See also 5 Bibliography 6 External links 6.1 General resources 6.2 Journals 6.3 Conferences

[edit] Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine. Human intuition cannot be entirely eliminated since the designer of the system must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data. Machine learning can be viewed as an attempt to automate parts of the scientific method. Some machine learning researchers create methods within the framework of Bayesian statistics.

[edit] Algorithm types

Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm. Common algorithm types include:

• Supervised learning --- where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function.
• Unsupervised learning --- which models a set of inputs: labeled examples are not available.
• Semi-supervised learning --- which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
• Reinforcement learning --- where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm.
• Transduction --- similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and test inputs which are available while training.
• Learning to learn --- where the algorithm learns its own inductive bias based on previous experience.

The performance and computational analysis of machine learning algorithms is a branch of theoretical computer science known as computational learning theory.

[edit] Machine learning topics

This list represents the topics covered on a typical machine learning course.

[edit] See also

[edit] Bibliography

• 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.
• Bhagat, P. M. (2005). Pattern Recognition in Industry, Elsevier. ISBN 0-08-044538-1.
• 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 [1].
• 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 [2].
• MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN 0-521-64298-1.
• Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7.
• Ian H. Witten and Eibe Frank "Data Mining: Practical machine learning tools and techniques" Morgan Kaufmann ISBN 0-12-088407-0.
• Sholom Weiss and Casimir Kulikowski (1991). Computer Systems That Learn, Morgan Kaufmann. ISBN 1-55860-065-5.
• Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.

[edit] External links

[edit] General resources

• UCI description
• MLnet Mailing List
• Index of Machine Learning Courses
• Kmining List of machine learning, data mining and KDD scientific conferences
• Book "Intelligent Systems and their Societies" by Walter Fritz.
• Machine learning at the Open Directory Project (suggest site) .
• MLpedia — wiki dedicated to machine learning.
• Machine Learning Tutorials & Resources Kardi Teknomo's Tutorials.
• The Encyclopedia of Computational Intelligence
• Machine Learning papers @ CiteSeer
• Multi-objective Machine Learning

[edit] Journals

• Journal of Machine Learning Research
• Machine Learning (journal)
• Neural Computation (journal)
• Pattern Recognition (Journal of the Pattern Recognition Society)
• Learning Inquiry: an academic journal centered on learning

[edit] Conferences

• International Conference on Machine Learning (ICML)
• International Conference on Machine Learning: Models, Technologies & Applications (MLMTA)
• Neural Information Processing Systems (NIPS) (conference)
• International Workshop on Artificial Intelligence & Statistics (AISTATS)
• Conference on Uncertainty in Artificial Intelligence
• The "Snowbird" Learning Workshop