منابع مشابه
Dyadic Decision Trees
These notes introduce a new kind of classifier called a dyadic decision tree (DDT). We also introduce a discrimination rule for learning a DDT that achieves the optimal rate of convergence, ER(ĥn) − R∗ = O(n−1/d), for the box-counting class, which was defined in the previous set of notes. This improves on the rate of ER(ĥn)−R = O(n−1/(d+2)) for the histogram sieve estimator from the previous no...
متن کاملTechnical Note: Algorithms for Optimal Dyadic Decision Trees
A dynamic programming algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, replacing the dynamic programming alg...
متن کاملAdaptive Minimax Classification with Dyadic Decision Trees
Decision trees are surprisingly adaptive in four important respects: They automatically (1) adapt to Tsybakov’s “noise” condition; (2) focus on data distributed on lower dimensional manifolds; (3) reject irrelevant features; (4) adapt to Bayes decision boundary smoothness. In this paper we examine a decision tree based on dyadic splits that adapts to each of these conditions to achieve minimax ...
متن کاملOptimal Decision Trees
We propose an Extreme Point Tabu Search (EPTS) algorithm that constructs globally optimal decision trees for classiication problems. Typically, decision tree algorithms are greedy. They optimize the misclassiication error of each decision sequentially. Our non-greedy approach minimizes the misclassiication error of all the decisions in the tree concurrently. Using Global Tree Optimization (GTO)...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2007
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-007-0717-6