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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.
A high-level view of the encoding algorithm is shown here: Initialize the dictionary to contain all strings of length one. Find the longest string W in the dictionary that matches the current input. Emit the dictionary index for W to output and remove W from the input. Add W followed by the next symbol in the input to the dictionary. Go to Step 2.
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 ...
The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction, to generate lower-dimensional embeddings for subsequent use by other machine learning algorithms. [1] Variants exist which aim to make the learned representations assume useful properties. [2]
Note how the algorithm is greedy, and so nothing is added to the table until a unique making token is found. The algorithm is to initialize last matching index = 0 and next available index = 1 and then, for each token of the input stream, the dictionary searched for a match: {last matching index, token}. If a match is found, then last matching ...
The Burrows–Wheeler transform (BWT, also called block-sorting compression) rearranges a character string into runs of similar characters. This is useful for compression, since it tends to be easy to compress a string that has runs of repeated characters by techniques such as move-to-front transform and run-length encoding.
Arithmetic coding is a more modern coding technique that uses the mathematical calculations of a finite-state machine to produce a string of encoded bits from a series of input data symbols. It can achieve superior compression compared to other techniques such as the better-known Huffman algorithm.
Empirically, for machine learning heuristics, choices of a function that do not satisfy Mercer's condition may still perform reasonably if at least approximates the intuitive idea of similarity. [6] Regardless of whether k {\displaystyle k} is a Mercer kernel, k {\displaystyle k} may still be referred to as a "kernel".