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Specifically, hybridization between DNA molecules is interpreted as computing the inner product between embedded vectors in a corresponding vector space, and our algorithm performs learning of a binary classifier in this vector space.
In this vector space, we can introduce a complex structure defining (2.3).
During the training process, both the ANN and SVM approximate a function (hyperplane) in this vector space, which is intended to separate the positive and the negative training examples.
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One of the extensions of this inequality is in the vector space settings.
With this purpose, we embed each of these series in the vector space (mathbb {R}^{2mathrm{d}}).
This function can be used to classify new data points in the vector space.
Because the vector space representation fails to capture the relationship between synonymous terms such as car and automobile - according each a separate dimension in the vector space.
represented in the vector space model using tf-idf scores.
In other words, in the vector space, most components of a text are zero-vectors.
Linear independence means that the coordinates are uniquely determined for any vector in the vector space.
The distance between the documents consists in the distance between the vectors scattered in the vector space.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com