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Matrix approximation and projective clustering via volume sampling
TLDR
This paper proves that the additive error drops exponentially by iterating the sampling in an adaptive manner, and gives a pass-efficient algorithm for computing low-rank approximation with reduced additive error. Expand
A spectral algorithm for learning mixture models
We show that a simple spectral algorithm for learning a mixture of k spherical Gaussians in Rn works remarkably well--it succeeds in identifying the Gaussians assuming essentially the minimumExpand
Algorithmic Prediction of Health-Care Costs
TLDR
Modern data-mining methods are utilized, specifically classification trees and clustering algorithms, along with claims data from over 800,000 insured individuals over three years, to provide rigorously validated predictions of health-care costs in the third year, based on medical and cost data from the first two years. Expand
A spectral algorithm for learning mixtures of distributions
  • S. Vempala, Grant J. Wang
  • Mathematics, Computer Science
  • The 43rd Annual IEEE Symposium on Foundations of…
  • 16 November 2002
We show that a simple spectral algorithm for learning a mixture of k spherical Gaussians in /spl Ropf//sup n/ works remarkably well - it succeeds in identifying the Gaussians assuming essentially theExpand
A divide-and-merge methodology for clustering
TLDR
A divide-and-merge methodology for clustering a set of objects that combines a top-down "divide" phase with a bottom-up "merge" phase is presented and a meta-search engine that uses this methodology to cluster results from web searches is presented. Expand
A divide-and-merge methodology for clustering
TLDR
A divide-and-merge methodology for clustering a set of objects that combines a top-down “divide" phase with a bottom-up “merge” phase is presented and the implementation of a meta-search engine that uses this methodology to cluster results from web searches is described. Expand
Matrix approximation and projective clustering via volume sampling
Frieze et al. [17] proved that a small sample of rows of a given matrix A contains a low-rank approximation D that minimizes ||A - D||F to within small additive error, and the sampling can be doneExpand
Matrix Approximation and Projective Clustering via Iterative Sampling
TLDR
The first result shows that with adaptive sampling in t rounds and O(k/ ) samples in each round, the additive error drops exponentially as ; the computation time is nearly linear in the number of nonzero entries. Expand
Clustering-Based Bidding Languages for Sponsored Search
TLDR
This paper states that broad matching has undesirable economic properties (such as the non-existence of equilibria) that can make it hard for an advertiser to determine how much to bid for a broad-matched keyword. Expand
Optimizing query rewrites for keyword-based advertising
TLDR
This work forms a family of graph covering problems whose goals are to suggest a subset of ads with the maximum benefit by suggesting rewrites for a given query by obtaining constant-factor approximation algorithms for these covering problems. Expand
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