P. Morin. An optimal randomized algorithm for d-variate zonoid depth. Submitted to Computational Geometry: Theory and Applications, September 2006.P. Morin, An optimal randomized algorithm for d-variate zonoid
This is optimal in this model. (See Schwarz [108].) Show moreView chapter Book 2000, Handbook of Computational GeometryMichiel Smid Chapter Parallel Computational Geometry: An Approach using Randomization Notation We say a randomized algorithm has a resource (time, space etc.) bound of O˜gn...
Greedy algorithm.This algorithm solves optimization problems by finding the locally optimal solution, hoping it is the optimal solution at the global level. However, it does not guarantee the most optimal solution. Recursive algorithm.This algorithm calls itself repeatedly until it solves a problem. R...
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensionsdoi:10.1145/1327452.1327494We present an algorithm,for the c-approximate ... A Andoni,P Indyk - 《Foundations of Computer Science Annual Symposium on》 被引量: 1603发表: 2008年 Newton Sketch: A Linear-time Optimizatio...
They are optimal inputs for machine learning algorithms. You transform your raw input data into features using one or more data transforms. For example, text data is transformed into a set of word counts and word combination counts. Once the features have been extracted from a raw data type ...
The performance of the algorithm was represented by an area under the curve of 0.70 (p < 0.01). In conclusion, this study supports the association of a large blastocyst size with higher implantation potential and suggests that automatically measured blastocyst morphometrics can be used as a ...
Utilizing a flexible optimization-driven framework, our algorithm approximates the globally optimal solution leading to high quality partitions of the feature space. We propose a novel method which can optimize for various clustering internal validation metrics and naturally determines the optimal number ...
We propose a algorithm to give a approximate solution of a minimal covering circle or ellipse of a set of points. The iterative algorithm is based on the optimal ellipse which best describe a given set of points.
An optimal approximation algorithm for Bayesian inference. Artificial Intel- ligence, 93(1-2):1-27, June 1997.P. Dagum, M. Luby, An optimal approximation algorithm for Bayesian inference, Artificial Intelligence 93 (1997) 1-27.P. Dagum and M. Luby. An optimal approximation algorithm for ...
The algorithm has been implemented and tested over several hundreds of random polygons with and without holes. The cardinality of the solutions provided is very near to, or coincident with, a polygon specific lower bound, and then suboptimal or optimal. In addition, our algorithm has been ...