Results 271 to 280 of about 136,861 (303)

Unsupervised Learning in Metagame

1999
The Metagame approach to computer game playing, introduced by Pell, involves writing programs that can play many games from some laxge class, rather thein programs speciailised to play just a single game such as chess. Metagame programs take the rules of a randomly generated game as input, then do some analysis of that game, and then play the game ...
Graham E. Farr, David R. Powell
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Unsupervised learning of action primitives

2010 10th IEEE-RAS International Conference on Humanoid Robots, 2010
Action representation is a key issue in imitation learning forhumanoids. With the recent finding of mirror neurons there has been agrowing interest in expressing actions as a combination meaningfulsubparts called primitives. Primitives could be thought of as analphabet for the human actions. In this paper we observe that humanactions and objects can be
San Mohan, Volker Krüger, Danica Kragic
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Unsupervised Learning of Relations

2010
Learning processes allow the central nervous system to learn relationships between stimuli. Even stimuli from different modalities can easily be associated, and these associations can include the learning of mappings between observable parameters of the stimuli.
Matthew Cook 0001, Florian Jug, Christoph Krautz, Angelika Steger   +3 more
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Unsupervised Learning

Unsupervised learning, an essential component of machine learning, has a substantial impact on the advancement and implementation of generative AI. Incorporating unsupervised learning into generative AI models has the potential to transform businesses by automating and improving creative processes.
Akshay Bhuvaneswari Ramakrishnan, S. Srijanani   +1 more
  +6 more sources

An Approach to Unsupervised Learning Classification

IEEE Transactions on Computers, 1975
In this correspondence, an approach to unsupervised pattern classifiers is discussed. The classifiers discussed here have the ability of obtaining the consistent estimates of unknown statistics of input patterns without knowing the a priori probability of each category's occurrence where the input patterns are of a mixture distribution.
Riichiro Mizoguchi, Masamichi Shimura
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XAI for unsupervised learning

Unsupervised learning algorithms detect inherent patterns and relationships in data without requiring predefined target variables. Although unsupervised learning algorithms have great capabilities, their decisions remain largely opaque, driving the need for explainability.
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A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning

ACM Computing Surveys, 2022
Mohanad Abukmeil, Stefano Ferrari, Angelo Genovese   +2 more
exaly  

Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage

Structural Health Monitoring, 2021
Zilong Wang, Young-Jin Cha
exaly  

Cluster-Guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification

IEEE Transactions on Image Processing, 2022
Mingkun Li, Chun-Guang Li, Jun Guo
exaly  

Unsupervised Learning

1999
Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years.
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