Results 11 to 20 of about 1,169,808 (377)
Superconstant Inapproximability of Decision Tree Learning
Annual Conference Computational Learning TheoryWe consider the task of properly PAC learning decision trees with queries. Recent work of Koch, Strassle, and Tan showed that the strictest version of this task, where the hypothesis tree $T$ is required to be optimally small, is NP-hard.
Caleb Koch, Carmen Strassle, Li-Yang Tan
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Decision Tree Learning in CEGIS-Based Termination Analysis [PDF]
International Conference on Computer Aided Verification, 2021 We present a novel decision tree-based synthesis algorithm of ranking functions for verifying program termination. Our algorithm is integrated into the workflow of CounterExample Guided Inductive Synthesis (CEGIS).
Satoshi Kura, Hiroshi Unno, I. Hasuo
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Cost-Sensitive Decision Tree Learning [PDF]
2019 Amity International Conference on Artificial Intelligence (AICAI), 2019 openalex +2 more sources
Achieving Verifiable Decision Tree Prediction on Hybrid Blockchains
Entropy, 2023 Machine learning has become increasingly popular in academic and industrial communities and has been widely implemented in various online applications due to its powerful ability to analyze and use data.
Moxuan Fu +5 more
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SAT-based Decision Tree Learning for Large Data Sets
AAAI Conference on Artificial Intelligence, 2021 Decision trees of low depth are beneficial for understanding and interpreting the data they represent. Unfortunately, finding a decision tree of lowest depth that correctly represents given data is NP-hard.
André Schidler, Stefan Szeider
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dtControl: decision tree learning algorithms for controller representation [PDF]
International Conference on Hybrid Systems: Computation and Control, 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
P. Ashok +5 more
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Classification Based on Decision Tree Algorithm for Machine Learning
Journal of Applied Science and Technology Trends, 2021 Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision ...
Bahzad Charbuty, Adnan Abdulazeez
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Evolutionary Learning of Interpretable Decision Trees
IEEE Access, 2023 69 pages, 31 figures, code available at: https://gitlab.com/leocus ...
Custode, Leonardo Lucio, Iacca, Giovanni
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Hard-ODT: Hardware-Friendly Online Decision Tree Learning Algorithm and System [PDF]
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020 Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable for learning large-scale datasets due to their stringent data storage ...
Zhe Lin, Sharad Sinha, Wei Zhang
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Pembentukan Model Pohon Keputusan pada Database Car Evaluation Menggunakan Statistik Chi-Square
Contemporary Mathematics and Applications (ConMathA), 2022 The study discusses problems related to the formation of a decision tree based on a collection of evaluation data records obtained from a number of car buyers. This secondary data was obtained from the UCL machine learning website.
Retno Maharesi
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