Shape Quantization and Recognition with Randomized Trees

By Yali Amit, Donald Geman. 1997

Subfields: Computer Vision and Pattern Recognition, Statistics and Probability

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Citations in the corpus

Random Forests (2001, methodology): “Amit and Geman (1997) grew shallow trees for handwritten character recognition using random selection from a large number of geometrically deﬁned features to deﬁne the split at each node.”
Local Linear Forests (2018, methodology): “2 Related Work Random forests were first introduced by Breiman [2001], building on the work of Breiman, Friedman, Stone, and Olshen [1984] on recursive partitioning (CART), Breiman [1996] on bagging, and Amit and Geman [1997] on randomized trees.”
Generalized random forests (2016, methodology): “In standard classification or regression forests as proposed by Breiman (2001), the prediction for a particular test point x is determined by averaging predictions across an ensemble of different trees (Amit and Geman, 1997; Breiman, 1996; Dietterich, 2000; Ho, 1998).”
Node harvest: simple and interpretable regression and classication (2009, background): “There has been substantial interest in the Random Forests framework for classiﬁcation and regression [Breiman (2001)], which builds partly upon the randomized tree idea in Amit and Geman (1997).”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, methodology): “The approach is due to Breiman (2001), which was based on prior ideas of Amit and Geman (1997) and Ho (1995, 1998).”
Randomized trees for real-time keypoint recognition (2005, methodology): “Following [9], if dl(p) denotes the posterior distribution in the node of tree Tl reached by a patch p, p is classified considering the average of the distributions dl(p):”
Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests (2015, unknown): “(2013b) as well as to other RF literature (Amit and Geman, 1997; Breiman, 2001).”
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (2008, background): “Linear Discriminant Analysis (76), vehicle windows float processed (17), containers (13), tableware (9), and headlamps (29).”
Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation (2015, methodology): “This method has been used in image-related applications (Amit and Geman, 1997), including medical image segmentation (Criminisi and Shotton, 2013;Criminisi et al., 2012).”
Decision trees (2013, background): “21 Much of this cross-fertilization is due to substantial cross-disciplinary work carried Overview wires.wiley.com/compstats out by Breiman.”
Image Classification using Random Forests and Ferns (2007, background): “These classifiers were first introduced in [1] and developed further in [6].”
Quantile Regression Forests (2006, methodology): “Random forests grows an ensemble of trees, employing random node and split point selection, inspired by Amit and Geman (1997). The prediction of random forests can then be seen as an adaptive neighborhood classification and regression procedure (Lin and Jeon, 2002). For every X = x, a set of weights”
Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics (2007, methodology): “In Amit and Mascaro (2001) a neural network was applied to the classiﬁcation problem, and in Amit and Geman (1997), decision trees were employed.”
Boosting Image Retrieval (2000, methodology): “Our approach is based on earlier work by [6] and, is similar in spirit to the features used by [1].”
Machine Learning - The Art and Science of Algorithms that Make Sense of Data (2012, background): “Random forests, usually attributed to Breiman (2001), combine bagged decision trees with random subspaces; similar ideas were developed by Ho (1995) and Amit and Geman (1997). These techniques are particularly useful to reduce the variance of low-bias models such as tree models.”
Consistency of Random Survival Forests (2008, unknown): “Although there are many variants of RF (Amit and Geman, 1997; Dietterich, 2000; Cutler and Zhao, 2001), the most popular, and the one we focus on here, is that described by Breiman in his software manual (Breiman, 2003).”