Results 281 to 290 of about 2,068,010 (305)
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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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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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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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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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Geometry- and Accuracy-Preserving Random Forest Proximities
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Kevin R Moon, Jake S Rhodes
exaly
Accuracy and diversity-aware multi-objective approach for random forest construction
Expert Systems With Applications, 2023Sabeur Aridhi +2 more
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