# Producte d'experts

Product of experts (PoE) és una tècnica d'aprenentatge automàtic. Modela una distribució de probabilitat combinant la sortida de diverses distribucions més simples. Va ser proposat per Geoffrey Hinton, juntament amb un algorisme per entrenar els paràmetres d'aquest sistema.
La idea bàsica és combinar diverses distribucions de probabilitat ("experts") multiplicant les seves funcions de densitat, fent que la classificació PoE sigui semblant a una operació "i". Això permet a cada expert prendre decisions sobre la base d'unes quantes dimensions sense haver de cobrir tota la dimensionalitat d'un problema.
Això està relacionat amb (però molt diferent d'un model de barreja, on es combinen diverses distribucions de probabilitat mitjançant una operació "o", que és una suma ponderada de les seves funcions de densitat.

## Referències

* ↑ Hinton, Geoffrey E. Neural Computation, 14, 8, 2002, pàg. 1771–1800. DOI: 10.1162/089976602760128018. PMID: 12180402 [Consulta: 25 octubre 2009].
* ↑ Williams, C. K. I.; Agakov, F. V. «Products of Gaussians and Probabilistic Minor Component Analysis». Neural Computation. MIT Press - Journals, 14, 5, 01-05-2002, pàg. 1169–1182. DOI: 10.1162/089976602753633439. ISSN: 0899-7667.
* ↑ «Products of Hidden Markov Models». [Consulta: 26 juliol 2023].

## Bibliografia

* M. Carreira-Perpinan and G.E. Hinton, On contrastive divergence learning, Tenth International Workshop on Artificial Intelligence and Statistics, Barbados, 2005.
* P. Comon, Independent component analysis, a new concept? Signal Processing, 36:287-314, 1994.
* M.E. Tipping and C.M. Bishop, Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61(3), 611–622, 1999.
* P.V. Gehler, A.D. Holub, and M. Welling, The rate adapting Poisson model for information retrieval and object recognition, ACM, 06 2006.
* G.E. Hinton, Training products of experts by minimizing contrastive divergence, Neural Computation, 14:1771--1800, 2002.
* G.E. Hinton, S. Osindero, and Y.W. Teh, A fast learning algorithm for deep belief nets, Neural Computation, 18:1527-1554, 2006.
* M.S. Lewicki and T.J. Sejnowski, Learning overcomplete representations, Neural Computation, 12:p.337-365, 2000.
* D.S. Plaut, S. Nowlan and G.E. Hinton, Experiments on learning by back-propagation, Technical report CMU-CS-86-126, Dept. Comp. Science, CMU, Pittsburgh, PA, 1986.
* S. Roweis, EM Algorithms for PCA and SPCA, Advances in Neural Information Processing Systems 10, pp.626-632, 1997.
* R.R. Salakhutdinov, A. Mnih, and G.E. Hinton, Restricted Boltzmann machines for collaborative filtering, Proceedings of the 21st International Conference on Machine Learning, 2007.
* Y.W. Teh and G.E. Hinton, Rate-coded restricted Boltzmann machines for face recognition, Advances in Neural Information Processing Systems, volume 13, 2001.
* Y.W. Teh, M. Welling, S. Osindero, and G.E. Hinton, Energy-based models for sparse overcomplete representations, Journal of Machine Learning Research - Special Issue on ICA, 4:1235--1260, 2003.
* M. Welling, F. Agakov, and C.K.I. Williams, Extreme components analysis, Advances in Neural Information Processing Systems, volume 16, Vancouver, Canada, 2003.
* M. Welling, G.E. Hinton, and S. Osindero, Learning sparse topographic representations with products of Student-t distributions, Advances in Neural Information Processing Systems, volume 15, Vancouver, Canada, 2002.
* M. Welling, M. Rosen-Zvi, and G.E. Hinton, Exponential family harmoniums with an application to information retrieval, Advances in Neural Information Processing Systems, volume 17, Vancouver, Canada, 2004.
* E. Xing, R. Yan, and A. Hauptman, Mining associated text and images with dual-wing harmoniums, Proc. Uncertainty in Artificial Intelligence 2005.

## Enllaços externs

* Article de producte d'experts a Scholarpedia