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» On the Autoreducibility of Random Sequences
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ICASSP
2009
IEEE
13 years 5 months ago
Applying discretized articulatory knowledge to dysarthric speech
This paper applies two dynamic Bayes networks that include theoretical and measured kinematic features of the vocal tract, respectively, to the task of labeling phoneme sequences ...
Frank Rudzicz
ICML
2004
IEEE
14 years 1 months ago
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses poin...
Hisashi Kashima, Yuta Tsuboi
ICPR
2008
IEEE
14 years 2 months ago
Direct 3-D shape recovery from image sequence based on multi-scale Bayesian network
We propose a new method for recovering a 3-D object shape from an image sequence. In order to recover high-resolution relative depth without using the complex Markov random field...
Norio Tagawa, Junya Kawaguchi, Shoichi Naganuma, K...
CSB
2003
IEEE
110views Bioinformatics» more  CSB 2003»
14 years 1 months ago
Prokaryote Phylogeny without Sequence Alignment: From Avoidance Signature to Composition Distance
A new and essentially simple method to reconstruct prokaryotic phylogenetic trees from their complete genome data without using sequence alignment is proposed. It is based on the ...
Bailin Hao, Ji Qi
BMCBI
2008
149views more  BMCBI 2008»
13 years 7 months ago
Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment s
Background: Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic d...
Olivier Bastien, Eric Maréchal