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Boosting algorithms can be based on convex or non-convex optimization algorithms. Convex algorithms, such as AdaBoost and LogitBoost, can be "defeated" by random noise such that they can't learn basic and learnable combinations of weak hypotheses. [19] [20] This limitation was pointed out by Long & Servedio in 2008.
Benson's algorithm; Berndt–Hall–Hall–Hausman algorithm; Bin covering problem; Bin packing problem; Bland's rule; Branch and bound; Branch and cut; Branch and price; Bregman Lagrangian; Bregman method; Broyden–Fletcher–Goldfarb–Shanno algorithm
It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [7] It works on Linux , Windows , macOS , and is available in Python , [ 8 ] R , [ 9 ] and models built using CatBoost can be used for predictions in C++ , Java ...
The multiplicative weights algorithm is also widely applied in computational geometry, [1] such as Clarkson's algorithm for linear programming (LP) with a bounded number of variables in linear time. [ 4 ] [ 5 ] Later, Bronnimann and Goodrich employed analogous methods to find Set Covers for hypergraphs with small VC dimension .
In machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani.. The original paper casts the AdaBoost algorithm into a statistical framework. [1]
In the C++ Standard Library, the algorithms library provides various functions that perform algorithmic operations on containers and other sequences, represented by Iterators. [1] The C++ standard provides some standard algorithms collected in the <algorithm> standard header. [2] A handful of algorithms are also in the <numeric> header.
A minimum spanning tree of a weighted planar graph.Finding a minimum spanning tree is a common problem involving combinatorial optimization. Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, [1] where the set of feasible solutions is discrete or can be reduced to a discrete set.
The bigfloat type improves on the C++ floating-point types by allowing for the significand (also commonly called mantissa) to be set to an arbitrary level of precision instead of following the IEEE standard. LEDA's real type allows for precise representations of real numbers, and can be used to compute the sign of a radical expression. [1]