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TCS
1998
14 years 9 months ago
An Improved Zero-One Law for Algorithmically Random Sequences
Results on random oracles typically involve showing that a class {X : P(X)} has Lebesgue measure one, i.e., that some property P(X) holds for “almost every X.” A potentially m...
Steven M. Kautz
ALENEX
2003
137views Algorithms» more  ALENEX 2003»
14 years 11 months ago
The Markov Chain Simulation Method for Generating Connected Power Law Random Graphs
Graph models for real-world complex networks such as the Internet, the WWW and biological networks are necessary for analytic and simulation-based studies of network protocols, al...
Christos Gkantsidis, Milena Mihail, Ellen W. Zegur...
SIAMCO
2000
104views more  SIAMCO 2000»
14 years 9 months ago
Law of the Iterated Logarithm for a Constant-Gain Linear Stochastic Gradient Algorithm
We study almost-sure limiting properties, taken as 0, of the finite horizon sequence of random estimates { 0, 1, 2, . . . , T/ } for the linear stochastic gradient algorithm n+1 ...
J. A. Joslin, A. J. Heunis
BMCBI
2006
139views more  BMCBI 2006»
14 years 9 months ago
Improvement in accuracy of multiple sequence alignment using novel group-to-group sequence alignment algorithm with piecewise li
Background: Multiple sequence alignment (MSA) is a useful tool in bioinformatics. Although many MSA algorithms have been developed, there is still room for improvement in accuracy...
Shinsuke Yamada, Osamu Gotoh, Hayato Yamana
ACL
2006
14 years 11 months ago
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...