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Although HDLC framing has an overhead of <1% in the average case, it suffers from a very poor worst-case overhead of 100%; for inputs that consist entirely of bytes that require escaping, HDLC byte stuffing will double the size of the input. The COBS algorithm, on the other hand, tightly bounds the worst-case overhead.
During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1] In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text. If the input text is long, the fixed-length vector ...
Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms.
A local branch predictor has a separate history buffer for each conditional jump instruction. It may use a two-level adaptive predictor. The history buffer is separate for each conditional jump instruction, while the pattern history table may be separate as well or it may be shared between all conditional jumps.
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.
In computer science, a data buffer (or just buffer) is a region of memory used to store data temporarily while it is being moved from one place to another. Typically, the data is stored in a buffer as it is retrieved from an input device (such as a microphone) or just before it is sent to an output device (such as speakers); however, a buffer may be used when data is moved between processes ...
The assignment problem is a fundamental combinatorial optimization problem. In its most general form, the problem is as follows: In its most general form, the problem is as follows: The problem instance has a number of agents and a number of tasks .
Machine learning algorithms train a model based on a finite set of training data. During this training, the model is evaluated based on how well it predicts the observations contained in the training set.