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TCS
1998
13 years 4 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»
13 years 6 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»
13 years 4 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»
13 years 4 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
13 years 6 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...