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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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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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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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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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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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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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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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