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For FIFO input buffers, a simple model of fixed-sized cells to uniformly distributed destinations, causes the throughput to be limited to 58.6% of the total as the number of links becomes large. [1] One way to overcome this limitation is by using virtual output queues. [2] Only switches with input buffering can suffer HOL blocking.
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.
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.
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 ...
In computing, vectored I/O, also known as scatter/gather I/O, is a method of input and output by which a single procedure call sequentially reads data from multiple buffers and writes it to a single data stream (gather), or reads data from a data stream and writes it to multiple buffers (scatter), as defined in a vector of buffers.
Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them.
Python mahotas, an open source computer vision package which includes an implementation of LBPs. OpenCV 's Cascade Classifiers support LBPs as of version 2. VLFeat , an open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation .
A number of policies have attempted to use perceptrons, markov chains or other types of machine learning to predict which line to evict. [28] [29] Learning augmented algorithms also exist for cache replacement. [30] [31]