Results 101 to 110 of about 1,084,163 (283)
, 2015 In order to speed-up classification models when facing a large number of categories, one usual approach consists in organizing the categories in a particular structure, this structure being then used as a way to speed-up the prediction computation. This is for example the case when using error-correcting codes or even hierarchies of categories.
Léon, Aurélia, Denoyer, Ludovic
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Simplifying decision trees [PDF]
International Journal of Man-Machine Studies, 1987 Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity and are therefore incomprehensible to experts.
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Scientific ReportsThe current power grid business handles massive data operations where data retrieval frequently encounters redundancy issues. Conventional decision tree-based methods struggle to achieve accurate data acquisition when facing redundant interference.
Wei Sun +5 more
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Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
Advanced Engineering Materials, EarlyView.Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani +2 more
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CUDT: A CUDA Based Decision Tree Algorithm
The Scientific World Journal, 2014 Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree.
Win-Tsung Lo +4 more
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Advanced Engineering Materials, EarlyView.In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi +4 more
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, 2001 This paper addresses the issue of the induction of orthogonal, oblique and multivariate decision trees. Algorithms proposed by other researchers use heuristic, usually based on the information gain concept, to induce decision trees greedily. These algorithms are often tailored for a given tree type ( e.g orthogonal), not being able to induce other ...
Llorà Fàbrega, Xavier +1 more
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On Decision Trees, Influences, and Learning Monotone Decision Trees
, 2004 In this note we prove that a monotone boolean function computable by a decision tree of size s has average sensitivity at most √ log2 s. As a consequence we show that monotone functions are learnable to constant accuracy under the uniform distribution in time polynomial in their decision tree size.
O'Donnell, Ryan, Servedio, Rocco Anthony
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Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
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