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TALG
2010
74views more  TALG 2010»
14 years 8 months ago
Comparison-based time-space lower bounds for selection
We establish the first nontrivial lower bounds on timespace tradeoffs for the selection problem. We prove that any comparison-based randomized algorithm for finding the median ...
Timothy M. Chan
ECML
1998
Springer
15 years 1 months ago
A Monotonic Measure for Optimal Feature Selection
Feature selection is a problem of choosing a subset of relevant features. Researchers have been searching for optimal feature selection methods. `Branch and Bound' and Focus a...
Huan Liu, Hiroshi Motoda, Manoranjan Dash
NIPS
2004
14 years 11 months ago
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms
We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...
78
Voted
TC
2008
14 years 9 months ago
On-Demand Solution to Minimize I-Cache Leakage Energy with Maintaining Performance
This paper describes a new on-demand wake-up prediction policy for reducing leakage power. The key insight is that branch prediction can be used to selectively wake up only the nee...
Sung Woo Chung, Kevin Skadron
JMLR
2006
99views more  JMLR 2006»
14 years 9 months ago
Worst-Case Analysis of Selective Sampling for Linear Classification
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...