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A rapidly exploring random tree (RRT) is an algorithm designed to efficiently search nonconvex, high-dimensional spaces by randomly building a space-filling tree.The tree is constructed incrementally from samples drawn randomly from the search space and is inherently biased to grow towards large unsearched areas of the problem.
Constructing skill trees (CST) is a hierarchical reinforcement learning algorithm which can build skill trees from a set of sample solution trajectories obtained from demonstration. CST uses an incremental MAP ( maximum a posteriori ) change point detection algorithm to segment each demonstration trajectory into skills and integrate the results ...
Random recursive tree, increasingly labelled trees, which can be generated using a simple stochastic growth rule. Treap or randomized binary search tree, a data structure that uses random choices to simulate a random binary tree for non-random update sequences; Rapidly exploring random tree, a fractal space-filling pattern used as a data ...
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.
In a random recursive tree, all such trees are equally likely. Alternatively, a random recursive tree can be generated by starting from a single vertex, the root of the tree, labeled 1 {\displaystyle 1} , and then for each successive label from 2 {\displaystyle 2} to n {\displaystyle n} choosing a random vertex with a smaller label to be its ...
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C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
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