Entropy-Minimization Algorithm: Difference between revisions
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** It can be applied by an [[Entropy-Minimization System]] (that solves an [[entropy-minimization task]]). | ** It can be applied by an [[Entropy-Minimization System]] (that solves an [[entropy-minimization task]]). | ||
* <B>See:</B> [[Maximum Entropy Algorithm]], [[Shannon Entropy]], [[EM Algorithm]]. | * <B>See:</B> [[Maximum Entropy Algorithm]], [[Shannon Entropy]], [[EM Algorithm]]. | ||
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Latest revision as of 03:21, 17 June 2021
An Entropy-Minimization Algorithm is an optimization algorithm that minimizes an entropy function.
- Context:
- It can be applied by an Entropy-Minimization System (that solves an entropy-minimization task).
- See: Maximum Entropy Algorithm, Shannon Entropy, EM Algorithm.
References
2005
- (Grandvalet & Bengio, 2004) ⇒ Yves Grandvalet, and Yoshua Bengio. (2004). “Semi-supervised Learning by Entropy Minimization.” In: Advances in Neural Information Processing Systems, pp. 529-536.
2003
- (Zhu, Ghahramani & Lafferty, 2003) ⇒ Xiaojin Zhu, Zoubin Ghahramani, and John D. Lafferty. (2003). “Semi-supervised learning using Gaussian fields and harmonic functions.” In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003).
- QUOTE: We also propose a method of parameter learning by entropy minimization, and show the algorithm’s ability to perform feature selection.
2002
- (Erdogmus & Principe, 2002) ⇒ Deniz Erdogmus, and Jose C. Principe. (2002). “An Error-entropy Minimization Algorithm for Supervised Training of Nonlinear Adaptive Systems." Signal Processing, IEEE Transactions on 50, no. 7