The randomized weighted majority algorithm is an algorithm in machine learning theory for aggregating expert predictions to a series of decision problems...

learning, weighted majority algorithm (WMA) is a meta learning algorithm used to construct a compound algorithm from a pool of prediction algorithms, which...

\eta =1/2} in weighted majority algorithm and allow 0 ≤ η ≤ 1 {\displaystyle 0\leq \eta \leq 1} in randomized weighted majority algorithm. The multiplicative...

method of random decision forests was first proposed by Salzberg and Heath in 1993, with a method that used a randomized decision tree algorithm to create...

Query-level feature Quickprop Radial basis function network Randomized weighted majority algorithm Reinforcement learning Repeated incremental pruning to produce...

k-NN smoothing, the k-NN algorithm is used for estimating continuous variables.[citation needed] One such algorithm uses a weighted average of the k nearest...

streaming algorithms process input data streams as a sequence of items, typically making just one pass (or a few passes) through the data. These algorithms are...

Algorithmic Game Theory, Cambridge University Press, pp. 79–101, ISBN 978-0-521-87282-9, MR 2391751; see 4.3.2 Randomized Weighted Majority Algorithm...

boosting algorithms. The original ones, proposed by Robert Schapire (a recursive majority gate formulation), and Yoav Freund (boost by majority), were not...

triangulation and then obtaining its dual. Direct algorithms include Fortune's algorithm, an O(n log(n)) algorithm for generating a Voronoi diagram from a set...

that used randomized decision tree algorithms to generate multiple different trees from the training data, and then combine them using majority voting to...

of stacking. Voting is another form of ensembling. See e.g. Weighted majority algorithm (machine learning). R: at least three packages offer Bayesian...

holds in the majority of the three solutions. This median always forms another solution to the instance. Feder (1994) describes an algorithm for efficiently...

particular, there are weighted likelihoods, weighted estimating equations, and weighted probability densities from which a majority of statistics are derived...

_{nb}^{*(c)}(t)\right]\leq 0} Such a stationary and randomized algorithm that bases decisions only on S(t) is called an S-only algorithm. It is often useful to assume that...

system with terms such as platform, engine, or algorithm) and sometimes only called "the algorithm" or "algorithm", is a subclass of information filtering system...

samples If N is less than 100%, the minority class samples will be randomized, as only a random subset of them will have SMOTE applied to them. Since the introduction...

A random ballot or random dictatorship is a randomized electoral system where the election is decided on the basis of a single randomly selected ballot...

weightedresult/blob/develop/src/JOS.WeightedResult/AliasMethodVose.cs C# implementation of Vose's algorithm. https://github.com/cdanek/KaimiraWeightedList C# implementation...

automata are two-dimensional, with his self-replicator implemented algorithmically. The result was a universal copier and constructor working within a...

(March 1995). "A critical point for random graphs with a given degree sequence". Random Structures & Algorithms. 6 (2–3): 161–180. CiteSeerX 10.1.1.24...

of the two measurements weighted by their respective information. It is worth noting that if x {\displaystyle {x}} is a random variable. The estimates...

nonlinear optimization problem is to use a randomized optimization method. Optimum solutions are found by generating random samples that satisfy the constraints...

K-means algorithm, where the number of clusters is set by the level of undersampling. Tomek links remove unwanted overlap between classes where majority class...

In 2004, Arindam Banerjee used a weighted-Bregman distance instead of KL-distance to design a Biclustering algorithm that was suitable for any kind of...

reduction is usually performed prior to applying a k-nearest neighbors (k-NN) algorithm in order to mitigate the curse of dimensionality. Feature extraction and...

uses a post-weighted scoring algorithm to rank the competitors on the Individual Rounds. While the problems on these tests are weighted according to...

A Tsetlin machine is an artificial intelligence algorithm based on propositional logic. A Tsetlin machine is a form of learning automaton collective for...

that particular network. The Bianconi–Barabási model has been extended to weighted networks displaying linear and superlinear scaling of the strength with...

most automatic redistricting rules, the shortest splitline algorithm will fail to create majority-minority districts, for both ethnic and political minorities...