Results 31 to 40 of about 4,693,052 (300)
IEEE Access, 2022 We propose a new ML model called Topological Forest that contains an ensemble of decision trees. Unlike a vanilla Random Forest, Topological Forest has a special training process that selects a smaller number of decision trees on a topological graph ...
Murat Ali Bayir +3 more
doaj +1 more source
Quantum Speedup Based on Classical Decision Trees [PDF]
Quantum, 2020 Lin and Lin \cite{LL16} have recently shown how starting with a classical query algorithm (decision tree) for a function, we may find upper bounds on its quantum query complexity. More precisely, they have shown that given a decision tree for a function $
Salman Beigi, Leila Taghavi
doaj +1 more source
Interpreting CNNs via Decision Trees [PDF]
Computer Vision and Pattern Recognition, 2018 This paper aims to quantitatively explain the rationales of each prediction that is made by a pre-trained convolutional neural network (CNN). We propose to learn a decision tree, which clarifies the specific reason for each prediction made by the CNN at ...
Quanshi Zhang +3 more
semanticscholar +1 more source
Design of Neuro-Fuzzy Decision Trees
MATEC Web of Conferences, 2016 In order to improve accuracy of fuzzy decision trees classification we propose a procedure of parameters adaptation by means of neural network training.
Abramova Tatyana
doaj +1 more source
Collapsing the Decision Tree: the Concurrent Data Predictor [PDF]
, 2021 A family of concurrent data predictors is derived from the decision tree classifier by removing the limitation of sequentially evaluating attributes. By evaluating attributes concurrently, the decision tree collapses into a flat structure. Experiments indicate improvements of the prediction accuracy.
arxiv +1 more source
Omnivariate decision trees [PDF]
IEEE Transactions on Neural Networks, 2001 Univariate decision trees at each decision node consider the value of only one feature leading to axis-aligned splits. In a linear multivariate decision tree, each decision node divides the input space into two with a hyperplane. In a nonlinear multivariate tree, a multilayer perceptron at each node divides the input space arbitrarily, at the expense ...
C.T. Yildiz, Ethem Alpaydin
openaire +3 more sources
Boosted Decision Trees and Applications
EPJ Web of Conferences, 2013 Decision trees are a machine learning technique more and more commonly used in high energy physics, while it has been widely used in the social sciences.
Coadou Yann
doaj +1 more source
Learning Explainable Decision Rules via Maximum Satisfiability
IEEE Access, 2020 Decision trees are a popular choice for providing explainable machine learning, since they make explicit how different features contribute towards the prediction.
Henrik E. C. Cao +2 more
doaj +1 more source
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012 Licensed copy is available on IEEE ...
Manwani, N, Sastry, PS
openaire +3 more sources
dtControl: Decision Tree Learning Algorithms for Controller Representation [PDF]
, 2020 Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely. Compared to representations using lookup tables or binary decision diagrams, decision trees are smaller and more explainable.
arxiv +1 more source