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» Combining Outputs from Multiple Machine Translation Systems
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CSB
2005
IEEE
189views Bioinformatics» more  CSB 2005»
15 years 3 months ago
Learning Yeast Gene Functions from Heterogeneous Sources of Data Using Hybrid Weighted Bayesian Networks
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
Xutao Deng, Huimin Geng, Hesham H. Ali
ICDAR
2007
IEEE
15 years 4 months ago
Hybrid Mathematical Symbol Recognition Using Support Vector Machines
Recognition of mathematical symbols is a challenging task, with a large set with many similar symbols. We present a support vector machine based hybrid recognition system that use...
Birendra Keshari, Stephen M. Watt
INTERSPEECH
2010
14 years 4 months ago
A spoken term detection framework for recovering out-of-vocabulary words using the web
Vocabulary restrictions in large vocabulary continuous speech recognition (LVCSR) systems mean that out-of-vocabulary (OOV) words are lost in the output. However, OOV words tend t...
Carolina Parada, Abhinav Sethy, Mark Dredze, Frede...
ISMB
2001
14 years 11 months ago
Molecular classification of multiple tumor types
Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished...
Chen-Hsiang Yeang, Sridhar Ramaswamy, Pablo Tamayo...
RECOMB
2003
Springer
15 years 10 months ago
A multi-expert system for the automatic detection of protein domains from sequence information
We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are ...
Niranjan Nagarajan, Golan Yona