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In SVM classification, a training example is represented using a vector of features and a label.
In 2006 Alexandrescu et al. [31] present a model where each word is represented as a vector of features.
The output file is a table that can be produced in four alternative formats, containing a vector of features for every object processed.
The partitioning can be based on the similarities or distances between the data points where each datum represents a vector of features of a frame.
After the face detection and normalization step, a suitable family of descriptors has to be chosen in order to build a vector of features able to encode distinctive local structures in the facial region.
To calculate the feature vector of an inner pocket point (IFV), the AFVs from its atomic neighborhood are aggregated using a simple aggregation function and concatenated with a vector of features computed specifically for that point from its local neighborhood.
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This phase aims to convert each word to a vector of feature values.
The probability distribution P(r| s, t) is given as (3) In Equation (3), F = { f1,…, f K } denotes a vector of feature functions: K is the number of feature functions; and w = { w1,…, w K } presents a weight vector of the feature functions.
Let T denote the size of the training set, the binary feature vector of the ith pair in the training set be denoted by X i, y i = 1 if the pair is annotated as positive and y i = −1 otherwise, w denote a vector of feature weights, of the same length as X i, θ denote a threshold parameter and λ denote a regularization parameter.
Each ChIP-seq peak was represented with a vector of binary features, where each feature was derived from one biological information source.
Some statistical results of the features along each analysis segment are used to determine a vector of summary features that characterizes the respective segment.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com