Results 281 to 290 of about 86,959 (305)
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Random Structures & Algorithms, 1994
AbstractA random recursive forest is defined as a union of random recursive trees. We find the expected number of trees in the uniform random recursive forest as well as the number of vertices of given degree, the maximum degree, the height of vertices, the order of branches, the root of the component containing a given vertex, and the last root of ...
Krystyna T. Balinska +2 more
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AbstractA random recursive forest is defined as a union of random recursive trees. We find the expected number of trees in the uniform random recursive forest as well as the number of vertices of given degree, the maximum degree, the height of vertices, the order of branches, the root of the component containing a given vertex, and the last root of ...
Krystyna T. Balinska +2 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2016
We introduce a broad family of decision trees, Composite Trees, whose leaf classifiers are selected out of a hypothesis set composed of p subfamilies with different complexities. We prove new data-dependent learning guarantees for this family in the multi-class setting.
Giulia DeSalvo, Mehryar Mohri
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We introduce a broad family of decision trees, Composite Trees, whose leaf classifiers are selected out of a hypothesis set composed of p subfamilies with different complexities. We prove new data-dependent learning guarantees for this family in the multi-class setting.
Giulia DeSalvo, Mehryar Mohri
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2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009
Random Forests (RFs) are frequently used in many computer vision and machine learning applications. Their popularity is mainly driven by their high computational efficiency during both training and evaluation while achieving state-of-the-art results. However, in most applications RFs are used off-line.
Amir Saffari +4 more
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Random Forests (RFs) are frequently used in many computer vision and machine learning applications. Their popularity is mainly driven by their high computational efficiency during both training and evaluation while achieving state-of-the-art results. However, in most applications RFs are used off-line.
Amir Saffari +4 more
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2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2020
This talk will present the ongoing work of developing a Chapel implementation of Random Forest, a popular ensembling learning method utilized both for predictive modeling and feature selection. Language features in Chapel make it possible to easily express shared-memory and distributed-memory implementations of this algorithm.
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This talk will present the ongoing work of developing a Chapel implementation of Random Forest, a popular ensembling learning method utilized both for predictive modeling and feature selection. Language features in Chapel make it possible to easily express shared-memory and distributed-memory implementations of this algorithm.
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Cybernetics and Systems, 2015
Conventional supervised statistical learning models aim to achieve high accuracy in predicting the value of an outcome measure based on a number of input measures. However, in many applications, some type of action is randomized on the observational units.
Leo Guelman +2 more
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Conventional supervised statistical learning models aim to achieve high accuracy in predicting the value of an outcome measure based on a number of input measures. However, in many applications, some type of action is randomized on the observational units.
Leo Guelman +2 more
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2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
Ensemble methods are considered among the state-of-the-art predictive modeling approaches. Applied to modern big data, these methods often require a large number of sub-learners, where the complexity of each learner typically grows with the size of the dataset. This phenomenon results in an increasing demand for storage space, which may be very costly.
Amichai Painsky, Saharon Rosset
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Ensemble methods are considered among the state-of-the-art predictive modeling approaches. Applied to modern big data, these methods often require a large number of sub-learners, where the complexity of each learner typically grows with the size of the dataset. This phenomenon results in an increasing demand for storage space, which may be very costly.
Amichai Painsky, Saharon Rosset
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Towards convergence rate analysis of random forests for classification
Artificial Intelligence, 2022Fan Xu, Zhi-Hua Zhou, Fan Xu
exaly
Random Forests for Spatially Dependent Data
Journal of the American Statistical Association, 2023Arkajyoti Saha, Sumanta Basu
exaly
Variable selection using random forests
Pattern Recognition Letters, 2010Robin Genuer, Jean-Michel Poggi
exaly
Navigating Random Forests and related advances in algorithmic modeling
Statistics Surveys, 2009David S Široký
exaly