Results 271 to 280 of about 86,959 (305)
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Pattern Recognition, 2022
Abstract Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze its consistency and privacy-preservation.
Jiawang Bai +5 more
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Abstract Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze its consistency and privacy-preservation.
Jiawang Bai +5 more
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Combinatorics, Probability and Computing, 1992
A forest ℱ(n, M) chosen uniformly from the family of all labelled unrooted forests with n vertices and M edges is studied. We show that, like the Érdős-Rényi random graph G(n, M), the random forest exhibits three modes of asymptotic behaviour: subcritical, nearcritical and supercritical, with the phase transition at the point M = n/2.
Tomasz Luczak 0001, Boris G. Pittel
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A forest ℱ(n, M) chosen uniformly from the family of all labelled unrooted forests with n vertices and M edges is studied. We show that, like the Érdős-Rényi random graph G(n, M), the random forest exhibits three modes of asymptotic behaviour: subcritical, nearcritical and supercritical, with the phase transition at the point M = n/2.
Tomasz Luczak 0001, Boris G. Pittel
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Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction
Cancer Informatics, 2015 Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of ...
Raziur Rahman +2 more
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Soft Computing, 2018
Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF).
Carlos Javier Mantas +3 more
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Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF).
Carlos Javier Mantas +3 more
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Journal of Insurance Medicine, 2017
For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become ...
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For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become ...
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Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, 2016
Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy. Our algorithm is initialized with a forest. Then the entire training data
Angshuman Paul, Dipti Prasad Mukherjee
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Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy. Our algorithm is initialized with a forest. Then the entire training data
Angshuman Paul, Dipti Prasad Mukherjee
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2013 2nd IAPR Asian Conference on Pattern Recognition, 2013
We present an effective image representation based on a new tree-structured coding technique called `random decomposition forests' (RDFs). Our method combines the merits of visual-word representations and random forests. The proposed RDF is able to decompose a local descriptor into multiple sets of visual words in a recursive and randomized manner.
Chun-Han Chien, Hwann-Tzong Chen
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We present an effective image representation based on a new tree-structured coding technique called `random decomposition forests' (RDFs). Our method combines the merits of visual-word representations and random forests. The proposed RDF is able to decompose a local descriptor into multiple sets of visual words in a recursive and randomized manner.
Chun-Han Chien, Hwann-Tzong Chen
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Random Forests and Kernel Methods
IEEE Transactions on Information Theory, 2016 International audienceRandom forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high dimensional set-tings.
Scornet, Erwan
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Proceedings of 3rd International Conference on Document Analysis and Recognition, 2002
Decision trees are attractive classifiers due to their high execution speed. But trees derived with traditional methods often cannot be grown to arbitrary complexity for possible loss of generalization accuracy on unseen data. The limitation on complexity usually means suboptimal accuracy on training data.
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Decision trees are attractive classifiers due to their high execution speed. But trees derived with traditional methods often cannot be grown to arbitrary complexity for possible loss of generalization accuracy on unseen data. The limitation on complexity usually means suboptimal accuracy on training data.
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WIREs Data Mining and Knowledge Discovery, 2011
AbstractRandom forests have emerged as a versatile and highly accurate classification and regression methodology, requiring little tuning and providing interpretable outputs. Here, we briefly outline the genesis of, and motivation for, the random forest paradigm as an outgrowth from earlier tree‐structured techniques.
Mark R. Segal, Yuanyuan Xiao
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AbstractRandom forests have emerged as a versatile and highly accurate classification and regression methodology, requiring little tuning and providing interpretable outputs. Here, we briefly outline the genesis of, and motivation for, the random forest paradigm as an outgrowth from earlier tree‐structured techniques.
Mark R. Segal, Yuanyuan Xiao
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