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- Unsupervised_learning abstract "In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning.Unsupervised learning is closely related to the problem of density estimation in statistics. However unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data. Many methods employed in unsupervised learning are based on data mining methods used to preprocess[citation needed] data.Approaches to unsupervised learning include: clustering (e.g., k-means, mixture models, hierarchical clustering), hidden Markov models, blind signal separation using feature extraction techniques for dimensionality reduction (e.g., principal component analysis, independent component analysis, non-negative matrix factorization, singular value decomposition).Among neural network models, the self-organizing map (SOM) and adaptive resonance theory (ART) are commonly used unsupervised learning algorithms. The SOM is a topographic organization in which nearby locations in the map represent inputs with similar properties. The ART model allows the number of clusters to vary with problem size and lets the user control the degree of similarity between members of the same clusters by means of a user-defined constant called the vigilance parameter. ART networks are also used for many pattern recognition tasks, such as automatic target recognition and seismic signal processing. The first version of ART was "ART1", developed by Carpenter and Grossberg (1988).".
- Unsupervised_learning wikiPageExternalLink ul.pdf.
- Unsupervised_learning wikiPageID "233497".
- Unsupervised_learning wikiPageRevisionID "605056189".
- Unsupervised_learning hasPhotoCollection Unsupervised_learning.
- Unsupervised_learning subject Category:Machine_learning.
- Unsupervised_learning comment "In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning.Unsupervised learning is closely related to the problem of density estimation in statistics.".
- Unsupervised_learning label "Apprendimento non supervisionato".
- Unsupervised_learning label "Apprentissage non supervisé".
- Unsupervised_learning label "Aprendizaje no supervisado".
- Unsupervised_learning label "Uczenie nienadzorowane".
- Unsupervised_learning label "Unsupervised learning".
- Unsupervised_learning label "Unüberwachtes Lernen".
- Unsupervised_learning label "Обучение без учителя".
- Unsupervised_learning label "التعلم غير المراقب".
- Unsupervised_learning label "教師なし学習".
- Unsupervised_learning label "非監督式學習".
- Unsupervised_learning sameAs Unüberwachtes_Lernen.
- Unsupervised_learning sameAs Aprendizaje_no_supervisado.
- Unsupervised_learning sameAs Apprentissage_non_supervisé.
- Unsupervised_learning sameAs Apprendimento_non_supervisionato.
- Unsupervised_learning sameAs 教師なし学習.
- Unsupervised_learning sameAs 자율_학습_(기계_학습).
- Unsupervised_learning sameAs Uczenie_nienadzorowane.
- Unsupervised_learning sameAs m.01hylt.
- Unsupervised_learning sameAs Q1152135.
- Unsupervised_learning sameAs Q1152135.
- Unsupervised_learning wasDerivedFrom Unsupervised_learning?oldid=605056189.
- Unsupervised_learning isPrimaryTopicOf Unsupervised_learning.