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A string-searching algorithm, sometimes called string-matching algorithm, is an algorithm that searches a body of text for portions that match by pattern. A basic example of string searching is when the pattern and the searched text are arrays of elements of an alphabet ( finite set ) Σ.
This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding. The type of feature that is used in feature engineering depends on the specific machine learning algorithm that is being used. Some machine learning algorithms, such as decision trees, can handle both numerical and categorical features.
Feature engineering in machine learning and statistical modeling involves selecting, creating, transforming, and extracting data features. Key components include feature creation from existing data, transforming and imputing missing or invalid features, reducing data dimensionality through methods like Principal Components Analysis (PCA), Independent Component Analysis (ICA), and Linear ...
Perhaps the most famous improvement is the bitap algorithm (also known as the shift-or and shift-and algorithm), which is very efficient for relatively short pattern strings. The bitap algorithm is the heart of the Unix searching utility agrep. A review of online searching algorithms was done by G. Navarro. [4]
In a typical document classification task, the input to the machine learning algorithm (both during learning and classification) is free text. From this, a bag of words (BOW) representation is constructed: the individual tokens are extracted and counted, and each distinct token in the training set defines a feature (independent variable) of each of the documents in both the training and test sets.
Byte pair encoding [1] [2] (also known as digram coding) [3] is an algorithm, first described in 1994 by Philip Gage, for encoding strings of text into tabular form for use in downstream modeling. [4] A slightly-modified version of the algorithm is used in large language model tokenizers. The original version of the algorithm focused on ...
IOB files have no place to put commonly-needed meta-data, such as the character encoding being used, the data source, internal location-markers, and so on. More powerful formats (most obviously XML , but even JSON or s-expressions ) can handle far more diverse annotations, have far less variation between implementations, and are often shorter ...
Some categories of algorithms include: String searching algorithms for finding a given substring or pattern; String manipulation algorithms; Sorting algorithms; Regular expression algorithms; Parsing a string; Sequence mining; Advanced string algorithms often employ complex mechanisms and data structures, among them suffix trees and finite ...