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At the time, the field of statistical machine translation was all but dead.
Dr. Knight acknowledges that statistical machine translation is far from perfect.
These problems drive fundamental research on statistical machine learning and data-mining algorithms.
Statistical machine translation -- in which computers essentially learn new languages on their own instead of being "taught" the languages by bilingual human programmers -- has taken off.
Despite the progress being made in statistical machine translation, some researchers remain skeptical, preferring to focus their efforts on language-specific translation techniques.
Phrases like these, called "N-grams" (with N representing the number of terms in a given phrase) are the basic building blocks of statistical machine translation.
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The software includes state-of-the-art statistical machine-learning algorithms and bundles them in a consistent framework, accommodating any experimental designs and quantitation strategies.
Hence, they soon were supplied by novel statistical, machine-learning methods, which use hundreds of free parameters extracted from databases of experimentally mapped topologies [ 13, 27].
Finally, human annotation data for building statistical/machine-learning approaches is still lacking.
An alternative approach based on Statistical Machine Learning (SML) to identify associated markers [ 64] was also applied.
The association mapping approach was applied, using General (GLM) and Multiple Linear (MLM) Models, as well as Statistical Machine Learning (SML).
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