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Statistical and machine learning models for predicting university dropout and scholarship impact. [PDF]

PLoS One
Romero S, Liao X.
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Minimal vertex covers of random trees

, 2004
Stéphane Coulomb
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Node classification in random trees

International Symposium on Intelligent Data Analysis, 2023
We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the tree data structure is associated with node attributes (typically high ...
Wouter W. L. Nuijten, V. Menkovski
semanticscholar   +1 more source

Rapidly-exploring Random Trees for Testing Automated Vehicles

International Conference on Intelligent Transportation Systems, 2019
One of the expectations from fully or partially automated vehicles is to never cause an accident and actively avoid dangerous situations. However, an automated vehicle may not be able to avoid all collisions, e.g., collisions caused by other vehicles ...
Cumhur Erkan Tuncali, Georgios Fainekos
semanticscholar   +1 more source

Efficient Sampling With Q-Learning to Guide Rapidly Exploring Random Trees

IEEE Robotics and Automation Letters, 2018
This letter presents a novel approach for efficient sampling of Rapidly-exploring Random Trees (RRTs) based upon learning a state-action value function (Q-function).
Jinwook Huh, Daniel D. Lee
semanticscholar   +1 more source

Random trees in a graph and trees in a random graph

Mathematical Proceedings of the Cambridge Philosophical Society, 1986
This paper treats two related sets of problems in the theory of random graphs. In Sections 2 and 3 we study random spanning subtrees of a complete graph (or, equivalently, random labelled trees). It is shown that the number of common edges of two such random trees asymptotically has a Poisson distribution with expectation 2.
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On random trees

Studia Scientiarum Mathematicarum Hungarica, 2002
In a one-parameter model for evolution of random trees strong law of large numbers and central limit theorem are proved for the number of vertices with low degree. The proof is based on elementary martingale theory.
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On the profile of random trees

Random Structures and Algorithms, 1997
Let T be a plane rooted tree with n nodes which is regarded as family tree of a Galton-Watson branching process conditioned on the total progeny. The profile of the tree may be described by the number of nodes or the number of leaves in layer , respectively. It is shown that these two processes converge weakly to Brownian excursion local time.
Bernhard Gittenberger, Michael Drmota
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