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» Algorithmic randomness of continuous functions
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BMCBI
2010
113views more  BMCBI 2010»
13 years 8 months ago
Improving performance of mammalian microRNA target prediction
Background: MicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA...
Hui Liu, Dong Yue, Yidong Chen, Shou-Jiang Gao, Yu...
ALT
2006
Springer
14 years 5 months ago
Active Learning in the Non-realizable Case
Most of the existing active learning algorithms are based on the realizability assumption: The learner’s hypothesis class is assumed to contain a target function that perfectly c...
Matti Kääriäinen
IJAR
2006
125views more  IJAR 2006»
13 years 8 months ago
Computing best-possible bounds for the distribution of a sum of several variables is NP-hard
In many real-life situations, we know the probability distribution of two random variables x1 and x2, but we have no information about the correlation between x1 and x2; what are ...
Vladik Kreinovich, Scott Ferson
JSA
2007
191views more  JSA 2007»
13 years 8 months ago
Automated memory-aware application distribution for Multi-processor System-on-Chips
Mapping of applications on a Multiprocessor System-on-Chip (MP-SoC) is a crucial step to optimize performance, energy and memory constraints at the same time. The problem is formu...
Heikki Orsila, Tero Kangas, Erno Salminen, Timo D....
ALGORITHMICA
1998
73views more  ALGORITHMICA 1998»
13 years 8 months ago
Linear Probing and Graphs
Mallows and Riordan showed in 1968 that labeled trees with a small number of inversions are related to labeled graphs that are connected and sparse. Wright enumerated sparse connec...
Donald E. Knuth