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» Universal Data Compression and Linear Prediction
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TIT
1998
96views more  TIT 1998»
13 years 7 months ago
Sequential Prediction of Individual Sequences Under General Loss Functions
Abstract— We consider adaptive sequential prediction of arbitrary binary sequences when the performance is evaluated using a general loss function. The goal is to predict on each...
David Haussler, Jyrki Kivinen, Manfred K. Warmuth
TIP
2011
123views more  TIP 2011»
13 years 2 months ago
Spatial Sparsity-Induced Prediction (SIP) for Images and Video: A Simple Way to Reject Structured Interference
We propose a prediction technique that is geared toward forming successful estimates of a signal based on a correlated anchor signal that is contaminated with complex interference...
Gang Hua, Onur G. Guleryuz
SODA
2008
ACM
110views Algorithms» more  SODA 2008»
13 years 8 months ago
Why simple hash functions work: exploiting the entropy in a data stream
Hashing is fundamental to many algorithms and data structures widely used in practice. For theoretical analysis of hashing, there have been two main approaches. First, one can ass...
Michael Mitzenmacher, Salil P. Vadhan
ESA
2005
Springer
107views Algorithms» more  ESA 2005»
14 years 29 days ago
Workload-Optimal Histograms on Streams
Histograms are used in many ways in conventional databases and in data stream processing for summarizing massive data distributions. Previous work on constructing histograms on da...
S. Muthukrishnan, Martin Strauss, X. Zheng
ICCAD
2009
IEEE
144views Hardware» more  ICCAD 2009»
13 years 5 months ago
Virtual probe: A statistically optimal framework for minimum-cost silicon characterization of nanoscale integrated circuits
In this paper, we propose a new technique, referred to as virtual probe (VP), to efficiently measure, characterize and monitor both inter-die and spatially-correlated intra-die va...
Xin Li, Rob A. Rutenbar, R. D. (Shawn) Blanton