# Machine learning

Machine learning

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

The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Hence, machine learning is closely related not only to data mining and statistics, but also to theoretical computer science.

Applications

Applications for machine learning include natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics, brain-machine interfaces 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.

Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine. But human intuition cannot be entirely eliminated, since the system's designer 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 methodFact|date=May 2008.

Some statistical machine learning researchers create methods within the framework of Bayesian statistics.

Algorithm types

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

* Supervised learning &mdash; in which 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 $\left[X_1, X_2, ldots X_N\right] ,$ into one of several classes by looking at several input-output examples of the function.
* Unsupervised learning &mdash; An agent which models a set of inputs: labelled examples are not available.
* Semi-supervised learning &mdash; which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
* Reinforcement learning &mdash; in which 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 &mdash; 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 &mdash; in which the algorithm learns its own inductive bias based on previous experience.

Theory

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.Because training sets are finite and the future is uncertain, learning theory usually does not yield absolute guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common.

In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

* Autonomous robot
* Compound term processing
* Computational intelligence
* Fuzzy logic
* Inductive logic programming
* Intelligent system
* Journal of Machine Learning Research
* Important publications in machine learning (computer science)
* List of numerical analysis software
*MLMTA Machine Learning: Models, Technologies & Applications
* Multi-label classification
* Neural Information Processing Systems (NIPS) (conference)
* Neural network software
* Pattern recognition
* Predictive analytics
* WEKA Open-source machine learning framework for pattern classification, regression, and clustering.

* Ethem Alpaydın (2004) "Introduction to Machine Learning (Adaptive Computation and Machine Learning)", MIT Press, ISBN 0262012111
* Christopher M. Bishop (2007) "Pattern Recognition and Machine Learning", Springer ISBN 0-387-31073-8.
* 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), [http://learning-from-data.com 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), [http://support-vector.ws 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). " [http://www.inference.phy.cam.ac.uk/mackay/itila/ 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.
* Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001). " [http://www-stat.stanford.edu/~tibs/ElemStatLearn/ The Elements of Statistical Learning] ", Springer. ISBN 0387952845.
* Vladimir Vapnik (1998). "Statistical Learning Theory". Wiley-Interscience, ISBN 0471030031.

* [http://scholarpedia.org/article/Encyclopedia_of_Computational_Intelligence The Encyclopedia of Computational Intelligence]
* [http://www.machinelearning.org/ International Machine Learning Society]
* [http://www.kmining.com/info_conferences.html Kmining List of machine learning, data mining and KDD scientific conferences]
* [http://mloss.org/about/ Machine Learning Open Source Software]
* [http://www.patternrecognition.co.za/tutorials.html Machine Learning Tutorials]

Wikimedia Foundation. 2010.

### Look at other dictionaries:

• Machine learning — Apprentissage automatique L apprentissage automatique (machine learning en anglais) est un des champs d étude de l intelligence artificielle. L apprentissage automatique fait référence au développement, à l analyse et à l implémentation de… …   Wikipédia en Français

• Machine Learning — Maschinelles Lernen ist ein Oberbegriff für die „künstliche“ Generierung von Wissen aus Erfahrung: Ein künstliches System lernt aus Beispielen und kann nach Beendigung der Lernphase verallgemeinern. Das heißt, es lernt nicht einfach die Beispiele …   Deutsch Wikipedia

• Machine Learning (journal) — Infobox Journal title = Machine Learning discipline = Machine learning abbreviation = website = http://www.springer.com/west/home/computer/artificial?SGWID=4 147 70 35726603 0 publisher = Springer country = USA history = 1986 to present ISSN =… …   Wikipedia

• Machine Learning Department — The Machine Learning Department (MLD) is a division of the School of Computer Science at Carnegie Mellon University in Pittsburgh, Pennsylvania, United States.External links* [http://www.ml.cmu.edu/ Machine Learning Department Official Website] …   Wikipedia

• Machine Learning — …   Википедия

• machine learning — noun A field concerned with the design and development of algorithms and techniques that allow computers to learn …   Wiktionary

• Machine learning —   An artificial system s ability to learn by doing and to incorporate this into its knowledge base …   International financial encyclopaedia

• Weka (machine learning) — Infobox Software name = Weka caption = Weka 3.5.5 with Explorer window open with Iris UCI dataset developer = University of Waikato latest release version = 3.4.13 (book), 3.5.8 (developer) latest release date = July 16, 2008 operating system =… …   Wikipedia

• Journal of Machine Learning Research — Infobox Journal discipline = Machine learning abbreviation = JMLR website = http://jmlr.csail.mit.edu/papers/ publisher = MIT Press and Microtome Publishing country = USA history = 2000 to present ISSN = 1533 7928The Journal of Machine Learning… …   Wikipedia

• Classification in machine learning — See also: Pattern recognition This section needs integrating with Statistical classification (Discuss). Integration means cross linking and distinguishing (to/from each other), or sometimes merging (if consensus suggests). In machine learning and …   Wikipedia