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Encyclopedia > Statistical learning

Machine learning is an area of artificial intelligence concerned with the development of techniques which allow computers to "learn". More specifically, machine learning is a method for creating computer programs by the analysis of data sets. Machine learning overlaps heavily with statistics, since both fields study the analysis of data. 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 are to be represented and what mechanisms will be used to search for a characterization of the data.


Machine learning has a wide spectrum of applications including search engines, medical diagnosis, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, game playing and robot locomotion.


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.
  • 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, tried to predict new outputs based on training inputs, training outputs, and new inputs.
  • 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 statistics known as computational learning theory.

Contents

Common Techniques

See also

Bibliography

  • Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0198538642
  • MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms (http://www.inference.phy.cam.ac.uk/mackay/itila/), Cambridge University Press. ISBN 0521642981

External links

  • Kmining (http://www.kmining.com/info_conferences.html) List of machine learning, data mining and KDD scientific conferences

  Results from FactBites:
 
Machine learning - Wikipedia, the free encyclopedia (1485 words)
Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm.
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
The performance and computational analysis of machine learning algorithms is a branch of statistics known as computational learning theory.
A Statistical Learning/Pattern Recognition Glossary (4913 words)
Active learning is thus an application of decision theory to the process of learning.
Since reinforcement learning requires exploration, it is often combined with active learning, though this is not essential.
Another possibility is to learn a graph structure between the partitions, as in the Growing Neural Gas.
  More results at FactBites »

 
 

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