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Machine learning
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Machine learning is the subfield of artificial intelligence that is concerned with the design and development of algorithms that allow computers to improve their performance over time based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically produce (induce) models, such as rules and patterns, from data. Hence, machine learning is closely related to fields such as data mining, statistics, inductive reasoning, pattern recognition, and theoretical computer science.
ications 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, software engineering and robot locomotion.
machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine.
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Encyclopedia
Machine learning is the subfield of artificial intelligence that is concerned with the design and development of algorithms that allow computers to improve their performance over time based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically produce (induce) models, such as rules and patterns, from data. Hence, machine learning is closely related to fields such as data mining, statistics, inductive reasoning, pattern recognition, and 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, software engineering 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 method.
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 — 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 into one of several classes by looking at several input-output examples of the function.
- Unsupervised learning — An agent which models a set of inputs: labelled examples are not available.
- Semi-supervised learning — which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
- Reinforcement learning — 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 — 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 — 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.
See also
Further reading
- Ethem Alpaydin (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN 0262012111
- Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer ISBN 0-387-31073-8.
- Ray Solomonoff, "" A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.
- 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.
- 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), , Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN 3-540-31681-7.
- KECMAN Vojislav (2001), , The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN 0-262-11255-8.
- MacKay, D. J. C. (2003). , 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). , Springer. ISBN 0387952845.
- Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 0471030031.
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