English
Related papers

Related papers: Newton trees for ideals in two variables and appli…

200 papers

There are several common ways to encode a tree as a matrix, such as the adjacency matrix, the Laplacian matrix (that is, the infinitesimal generator of the natural random walk), and the matrix of pairwise distances between leaves. Such…

Populations and Evolution · Quantitative Biology 2007-05-23 Frederick A. Matsen , Steven N. Evans

Decision trees are renowned for their ability to achieve high predictive performance while remaining interpretable, especially on tabular data. Traditionally, they are constructed through recursive algorithms, where they partition the data…

Machine Learning · Computer Science 2024-08-27 Yufan Zhuang , Liyuan Liu , Chandan Singh , Jingbo Shang , Jianfeng Gao

Univariate polynomial root-finding is both classical and important for modern computing. Frequently one seeks just the real roots of a polynomial with real coefficients. They can be approximated at a low computational cost if the polynomial…

Numerical Analysis · Mathematics 2014-07-01 Victor Y. Pan

The Aho, Hopcroft and Ullman (AHU) algorithm has been the state of the art since the 1970s for determining in linear time whether two unordered rooted trees are isomorphic or not. However, it has been criticized (by Campbell and Radford)…

Data Structures and Algorithms · Computer Science 2024-02-13 Florian Ingels

We explore a physical model of ordered sums of integers as trains of rods. The trains for a fixed, possibly infinite, set of rod lengths naturally correspond to nodes in a tree; relations among finite linear recursions encoded in the…

Combinatorics · Mathematics 2025-10-16 Ethan D. Bolker , Debra K. Borkovitz , Katelyn Lee

Regression trees have emerged as a preeminent tool for solving real-world regression problems due to their ability to deal with nonlinearities, interaction effects and sharp discontinuities. In this article, we rather study regression trees…

Machine Learning · Statistics 2025-11-14 Nathan Wycoff

The multiplicative Newton-like method developed by the author et al. is extended to the situation where the dynamics is restricted to the orthogonal group. A general framework is constructed without specifying the cost function. Though the…

Machine Learning · Computer Science 2007-05-23 Toshinao Akuzawa

An alphabetic binary tree formulation applies to problems in which an outcome needs to be determined via alphabetically ordered search prior to the termination of some window of opportunity. Rather than finding a decision tree minimizing…

Information Theory · Computer Science 2009-03-28 Michael B. Baer

In enumerative combinatorics, it is often a goal to enumerate both labeled and unlabeled structures of a given type. The theory of combinatorial species is a novel toolset which provides a rigorous foundation for dealing with the…

Combinatorics · Mathematics 2013-12-03 Andy Hardt , Pete McNeely , Tung Phan , Justin M. Troyka

Decision trees and randomized forests are widely used in computer vision and machine learning. Standard algorithms for decision tree induction optimize the split functions one node at a time according to some splitting criteria. This greedy…

Machine Learning · Computer Science 2015-11-13 Mohammad Norouzi , Maxwell D. Collins , Matthew Johnson , David J. Fleet , Pushmeet Kohli

Quasi-trees generalize trees in that the unique "path" between two nodes may be infinite and have any countable order type. They are used to define the rank-width of a countable graph in such a way that it is equal to the least upper-bound…

Logic in Computer Science · Computer Science 2023-06-22 Bruno Courcelle

This work presents an approach to automatically induction for non-greedy decision trees constructed from neural network architecture. This construction can be used to transfer weights when growing or pruning a decision tree, allowing…

Machine Learning · Statistics 2019-12-10 Chapman Siu

Given i.i.d. data from an unknown distribution, we consider the problem of predicting future items. An adaptive way to estimate the probability density is to recursively subdivide the domain to an appropriate data-dependent granularity. A…

Probability · Mathematics 2009-12-30 Marcus Hutter

Given i.i.d. data from an unknown distribution, we consider the problem of predicting future items. An adaptive way to estimate the probability density is to recursively subdivide the domain to an appropriate data-dependent granularity. A…

Statistics Theory · Mathematics 2007-06-13 Marcus Hutter

Solving semiparametric models can be computationally challenging because the dimension of parameter space may grow large with increasing sample size. Classical Newton's method becomes quite slow and unstable with intensive calculation of…

Computation · Statistics 2021-08-19 Yucong Lin , Jinhua Su , Yang Liu , Jue Hou , Feifei Wang

Besides serving as prediction models, classification trees are useful for finding important predictor variables and identifying interesting subgroups in the data. These functions can be compromised by weak split selection algorithms that…

Applications · Statistics 2010-11-03 Wei-Yin Loh

Using invariants from commutative algebra to count geometric objects is a basic idea in singularities. For example, the multiplicity of an ideal is used to count points of intersection of two analytic sets at points of non-transverse…

Algebraic Geometry · Mathematics 2007-05-23 Terence Gaffney

We consider fixed-point equations for probability measures charging measured compact metric spaces that naturally yield continuum random trees. On the one hand, we study the existence/uniqueness of the fixed-points and the convergence of…

Probability · Mathematics 2021-05-05 Nicolas Broutin , Henning Sulzbach

The arithmetic of the natural numbers can be extended to arithmetic operations on planar binary trees. This gives rise to a non-commutative arithmetic theory. In this exposition, we describe this arithmetree, first defined by Loday, and…

Combinatorics · Mathematics 2008-09-26 Adriano Bruno , Dan Yasaki

Ren et al. recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors…

Computer Vision and Pattern Recognition · Computer Science 2018-09-05 Nenad Markuš , Ivan Gogić , Igor S. Pandžić , Jörgen Ahlberg