Journal ArticleDOI
Random search in the presence of noise, with application to machine learning
Sidney Yakowitz,E. Lugosi +1 more
TLDR
A search for the global minimum of a function is proposed; the search is on the basis of sequential noisy measurements and the search plan is shown to be convergent in probability to a set of minimizers.Abstract:
A search for the global minimum of a function is proposed; the search is on the basis of sequential noisy measurements. Because no unimodality assumptions are made, stochastic approximation and other well-known methods are not directly applicable. The search plan is shown to be convergent in probability to a set of minimizers. This study was motivated by investigations into machine learning. This setting is explained, and the methodology is applied to create an adaptively improving strategy for 8-puzzle problems.read more
Citations
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Journal ArticleDOI
A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization
TL;DR: A modification of the simulated annealing algorithm designed for solving discrete stochastic optimization problems that uses a constant (rather than decreasing) temperature for estimating the optimal solution and shows that both variants of the method are guaranteed to converge almost surely to the set of global optimal solutions.
Journal ArticleDOI
Selecting concise training sets from clean data
Mark Plutowski,Halbert White +1 more
TL;DR: Experimental results indicate that training upon exemplars selected in this fashion can save computation in general purpose use as well, and its use during network training is demonstrated.
Journal ArticleDOI
A method for discrete stochastic optimization
TL;DR: In this article, the authors present two versions of a new iterative method for solving discrete stochastic optimization problems where the objective function is evaluated using transient or steady-state simulation.
Journal ArticleDOI
Accelerating the convergence of random search methods for discrete stochastic optimization
TL;DR: A new variant of the stochastic comparison method is proved that it is guaranteed to converge almost surely to the set of global optimal solutions and a result is presented that demonstrates that this method is likely to perform well in practice.
Journal ArticleDOI
Adaptive problem-solving for large-scale scheduling problems: a case study
Jonathan Gratch,Steve Chien +1 more
TL;DR: In this article, a learning system explores a space of possible heuristic methods for one well-suited to the eccentricities of the given domain and problem distribution, and identifies strategies that not only decrease the amount of CPU time required to produce schedules, but also increase the percentage of problems that are solvable within computational resource limitations.
References
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Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Book
Principles of Artificial Intelligence
TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Journal ArticleDOI
Asymptotically efficient adaptive allocation rules
Tze Leung Lai,Herbert Robbins +1 more
Journal ArticleDOI
Stochastic Estimation of the Maximum of a Regression Function
J. Kiefer,Jacob Wolfowitz +1 more
TL;DR: In this article, the authors give a scheme whereby, starting from an arbitrary point, one obtains successively $x_2, x_3, \cdots$ such that the regression function converges to the unknown point in probability as n \rightarrow \infty.
Journal ArticleDOI
Weak and strong uniform consistency of kernel regression estimates
Y. P. Mack,Bernard W. Silverman +1 more
TL;DR: In this article, under mild conditions on the window, the bandwidth and the underlying distribution of the bivariate observations, the weak and strong uniform convergence rates on a bounded interval were obtained.