Next:
Complexity Results on Learning
Up:
Online Library of Jeff
Previous:
Space-Efficient Framework for Top-k
LEARNING, PREDICTION, ESTIMATION, CACHING, AND PREFETCHING
Complexity Results on Learning by Neural Nets
Learning in Parallel
Optimal Prefetching via Data Compression
Practical Prefetching via Data Compression
Optimal Prediction for Prefetching in the Worst Case
Using Vapnik-Chervonenkis Dimension to Analyze the Testing Complexity of Program Segments
A Theory for Memory-Based Learning
Coping with Uncertainty in Map Learning
Adaptive Disk Spindown via Optimal Rent-to-Buy in Probabilistic Environments
Application-Controlled Paging for a Shared Cache
Estimating Alphanumeric Selectivity in the Presence of Wildcards
Competitive Analysis of Buffer Management Algorithms for Parallel I/O Systems
Wavelet-Based Histograms for Selectivity Estimation
Scalable Mining for Classification Rules in Relational Databases
Data Cube Approximation and Histograms via Wavelets
Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets
Dynamic Maintenance of Wavelet-Based Histograms
XPathLearner: An On-Line Self-Tuning Markov Histogram for XML Path Selectivity Estimation
Lexicographically Optimal Smoothing for Broadband Traffic Multiplexing
SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads
Online Algorithms for Prefetching and Caching in Parallel Disks
CXHist: An On-line Classification-based Histogram for XML String Selectivity Estimation
Jeff Vitter
2009-11-09
