Stable marriage problem - Wikipedia
In mathematics, economics, and computer science, the stable marriage problem (also stable matching problem) is the problem of finding a stable matching between two equally sized sets of elements given an ordering of preferences for each element. A matching is a bijection from the elements of one set to the elements of the other set. A matching is not stable if: There is an element A of the first m
Flood fill - Wikipedia
fn fill(x, y): if not Inside(x, y) then return let s = new empty stack or queue Add (x, y) to s while s is not empty: Remove an (x, y) from s let lx = x while Inside(lx - 1, y): Set(lx - 1, y) lx = lx - 1 while Inside(x, y): Set(x, y) x = x + 1 scan(lx, x - 1, y + 1, s) scan(lx, x - 1, y - 1, s) fn scan(lx, rx, y, s): let span_added = false for x in lx .. rx: if not Inside(x, y): span_added = fals
Universal approximation theorem - Wikipedia
This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. (July 2023) (Learn how and when to remove this message) In the mathematical theory of artificial neural networks, universal approximation theorems are theorems[1][2] of the following form: Given a family of neural networks, for e
Box blur - Wikipedia
An example of an image blurred using a box blur. A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter. A 3 by 3 box blur ("radius 1") can be written as matrix Due to its property of using equal weigh
English phrasal verbs - Wikipedia
In the traditional grammar of Modern English, a phrasal verb typically constitutes a single semantic unit consisting of a verb followed by a particle (examples: turn down, run into or sit up), sometimes collocated with a preposition (examples: get together with, run out of or feed off of). Phrasal verbs ordinarily cannot be understood based upon the meanings of the individual parts alone but must
Multinomial logistic regression - Wikipedia
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes.[1] That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-val
Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo - Wikipedia
Simplified parse tree S = sentence NP = noun phrase RC = relative clause VP = verb phrase PN = proper noun N = noun V = verb City of Buffalo, New York American bison, colloquially referred to as buffalo "Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo" is a grammatically correct sentence in English that is often presented as an example of how homonyms and homophones can be used to
Locality-sensitive hashing - Wikipedia
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability.[1] (The number of buckets is much smaller than the universe of possible input items.)[1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search. It differs from conventio
ID3 algorithm - Wikipedia
Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches. In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan[1] used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm, and is typically used in the machine learning and
Reinforcement learning - Wikipedia
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from superv
Q-learning - Wikipedia
Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.[1] For any finite Markov decision process, Q-learning finds an optimal policy in the sense of maximizing the expected
Treebank - Wikipedia
This article's use of external links may not follow Wikipedia's policies or guidelines. Please improve this article by removing excessive or inappropriate external links, and converting useful links where appropriate into footnote references. (November 2017) (Learn how and when to remove this message) This article needs additional citations for verification. Please help improve this article by add
Arg max - Wikipedia
This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Arg max" – news · newspapers · books · scholar · JSTOR (October 2014) (Learn how and when to remove this message) As an example, both unnormalised and normalised sinc functions above have of {0} because
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CYK algorithm - Wikipedia