They were followed by systems combining rules for some PHI categories with learning for others, and then by systems purely based on machine learning without regular expression template features or rules, and finally by purely rule-based systems [ 13].
HypertenGene applies a maximum entropy model with a set of features, such as n-gram, chunk, parse tree and template features.
In particular, we found it impractical to design a set of template features for our data and to build a sufficiently large training set, both of which are necessary for any supervised method.
Our threading model defines the conditional probability of a given s and t as follows: Meanwhile, Z s, t) is a normalizing factor and F (a i −1 → a i | s, t) is a function that calculates the log-likelihood of the state transition from a i −1 to a i given target and template features at position i.
Each template features an interesting fact about the type of bird.
Second, the neighborhood statistical characteristics for each pixel are calculated as its template features.
Template features that induce RT pausing very likely contribute to the observed template switching events in gag during minus-strand synthesis.
First, the neighbourhood characteristics for every pixel in a blurred infrared image are computed as the template features of each pixel.
In the i2b2 de-identification challenge, systems based on machine learning with regular expression template features for all PHI categories performed best.
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