A History of Algorithms: From the Pebble to the Microchip - download pdf or read online

By Jean-Luc Chabert, C. Weeks, E. Barbin, J. Borowczyk, J.-L. Chabert, M. Guillemot, A. Michel-Pajus, A. Djebbar, J.-C. Martzloff

ISBN-10: 3540633693

ISBN-13: 9783540633693

A resource publication for the historical past of arithmetic, yet one that deals a special viewpoint via focusinng on algorithms. With the advance of computing has come an awakening of curiosity in algorithms. usually missed through historians and sleek scientists, extra all in favour of the character of recommendations, algorithmic techniques end up to were instrumental within the improvement of basic principles: perform ended in thought simply up to the opposite direction around. the aim of this ebook is to provide a old history to modern algorithmic perform.

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Extra resources for A History of Algorithms: From the Pebble to the Microchip

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Journal of Machine Learning Research, 4:119– 155, 2003. B. Tenenbaum and V. de Silva. Sparse multi-dimensional scaling using landmark points. In preparation. B. Tenenbaum, V. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319–2323, 2000. [49] R. Tibshirani. Principal curves revisited. Statistics and Computing, 2:183–190, 1992. [50] M. Trosset. Applications of multidimensional scaling to molecular conformation. Computing Science and Statistics, (29):148–152, 1998.

T. K. Saul. Non linear dimensionality reduction by locally linear embedding. Science, 290:2323–2326, 2000. T. K. Saul. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research, 4:119– 155, 2003. B. Tenenbaum and V. de Silva. Sparse multi-dimensional scaling using landmark points. In preparation. B. Tenenbaum, V. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319–2323, 2000. [49] R. Tibshirani.

53] Kilian Q. Weinberger, Fei Sha, and Lawrence K. Saul. Learning a kernel matrix for nonlinear dimensionality reduction. In ICML ’04: Proceedings of the twenty-first international conference on Machine learning, p. 106, New York, NY, USA, 2004. ACM. Q. K. Saul. An introduction to nonlinear dimensionality reduction by maximum variance unfolding. In AAAI, 2006. Q. K. Saul. Unsupervised learning of image manifolds by semidefinite programming. International Journal of Computer Vision, 70(1):77–90, 2006.

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A History of Algorithms: From the Pebble to the Microchip by Jean-Luc Chabert, C. Weeks, E. Barbin, J. Borowczyk, J.-L. Chabert, M. Guillemot, A. Michel-Pajus, A. Djebbar, J.-C. Martzloff


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