Difference between revisions of "Unsupervised Learning Task"

From GM-RKB
Jump to: navigation, search
m (Text replacement - "“" to "“")
Line 70: Line 70:
 
__NOTOC__
 
__NOTOC__
 
[[Category:Concept]] [[Category:Machine Learning]]
 
[[Category:Concept]] [[Category:Machine Learning]]
 +
 +
=== 2019 ===
 +
* (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/unsupervised_learning Retrieved:2019-12-4.
 +
** '''Unsupervised learning''' is a type of self-organized [[Hebbian learning]] that helps find previously unknown patterns in data set without pre-existing labels. It is also known as [[self-organization]] and allows modeling [[Probability density function|probability densities]] of given inputs.<ref name="Hinton99a"></ref> It is one of the main three categories of machine learning, along with [[supervised learning|supervised]] and [[reinforcement learning]]. [[Semi-supervised learning]] has also been described, and is a hybridization of supervised and unsupervised techniques. <P> Two of the main methods used in unsupervised learning are [[principal component analysis|principal component]] and [[cluster analysis]]. [[Cluster analysis]] is used in unsupervised learning to group, or segment, datasets with shared attributes in order to extrapolate algorithmic relationships.  Cluster analysis is a branch of [[machine learning]] that groups the data that has not been [[labeled data|labelled]], classified or categorized. Instead of responding to feedback, cluster analysis identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. This approach helps detect anomalous data points that do not fit into either group. A central application of unsupervised learning is in the field of [[density estimation]] in [[statistics]],<ref name="JordanBishop2004"></ref> though unsupervised learning encompasses many other domains involving summarizing and explaining data features. It could be contrasted with supervised learning by saying that whereas supervised learning intends to infer a [[conditional probability distribution]] <math display="inline">p_X(x\,|\,y)</math> conditioned on the label <math display="inline">y</math> of input data; unsupervised learning intends to infer an [[a priori probability]] distribution <math display="inline">p_X(x)</math>. <P> [[Generative adversarial networks]] can also be used with unsupervised learning, though they can also be applied to supervised and reinforcement techniques.

Revision as of 18:54, 4 December 2019

An Unsupervised Learning Task is a data-driven learning task with no labeled training cases.



References

2017a

2017b

2017C

2011

2009

2008

  • (Redei, 2008) ⇒ George P. Rédei. (2008). "Unsupervised Learning". In: Encyclopedia of Genetics, Genomics, Proteomics and Informatics pp 2067-2067
    • QUOTE: Identifies new, so far undetected, shared pattern(s) of sequences in macromolecules and determines the positive and negative representatives of the pattern(s). The information permits correlations between structure and function in languages as well as in proteins without direct human intervention in the details

2000

1998


2019

  • (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/unsupervised_learning Retrieved:2019-12-4.
    • Unsupervised learning is a type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels. It is also known as self-organization and allows modeling probability densities of given inputs.[3] It is one of the main three categories of machine learning, along with supervised and reinforcement learning. Semi-supervised learning has also been described, and is a hybridization of supervised and unsupervised techniques.

      Two of the main methods used in unsupervised learning are principal component and cluster analysis. Cluster analysis is used in unsupervised learning to group, or segment, datasets with shared attributes in order to extrapolate algorithmic relationships. Cluster analysis is a branch of machine learning that groups the data that has not been labelled, classified or categorized. Instead of responding to feedback, cluster analysis identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. This approach helps detect anomalous data points that do not fit into either group. A central application of unsupervised learning is in the field of density estimation in statistics,[1] though unsupervised learning encompasses many other domains involving summarizing and explaining data features. It could be contrasted with supervised learning by saying that whereas supervised learning intends to infer a conditional probability distribution [math]p_X(x\,|\,y)[/math] conditioned on the label [math]y[/math] of input data; unsupervised learning intends to infer an a priori probability distribution [math]p_X(x)[/math].

      Generative adversarial networks can also be used with unsupervised learning, though they can also be applied to supervised and reinforcement techniques.

  • 1.0 1.1 Jordan, Michael I.; Bishop, Christopher M. (2004). “Neural Networks". In Allen B. Tucker. Computer Science Handbook, Second Edition (Section VII: Intelligent Systems). Boca Raton, FL: Chapman & Hall/CRC Press LLC. ISBN 1-58488-360-X.
  • Acharyya, Ranjan (2008); A New Approach for Blind Source Separation of Convolutive Sources, (this book focuses on unsupervised learning with Blind Source Separation)
  • Cite error: Invalid <ref> tag; no text was provided for refs named Hinton99a