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Outgoing claims: the papers this work cites, builds on, or argues with.
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Objective Bayesianism and Geometry
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Multiple decision trees
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Uncertainty in Artificial Intelligence
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Simplifying decision trees
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An Empirical Comparison of Pruning Methods for Decision Tree Induction
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Boolean Feature Discovery in Empirical Learning
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Incremental Induction of Decision Trees
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Knowledge-Based Systems for Industrial Control
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Cognitive models from subcognitive skills
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Optimal partitioning for classification and regression trees
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Assessing Credit Card Applications Using Machine Learning
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Hierarchical Bayesian Analysis Using Monte Carlo Integration: Computing Posterior Distributions When There are Many Possible Models
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Bayesian Classification and Regression Trees
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Regression Trees
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An introduction to decision tree modeling
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Relational Planning, Learning, and Probabilistic Reasoning
inferred← citeshttps://doi.org/10.1017/9781108164085.018openalex-snapshot
Mathematical Preliminaries and Notation
inferred← citeshttps://doi.org/10.1017/cbo9780511794797.003openalex-snapshot
Agent Architectures and Hierarchical Control
inferred← citeshttps://doi.org/10.1017/cbo9780511794797.006openalex-snapshot
Propositions and Inference
inferred← citeshttps://doi.org/10.1017/cbo9780511794797.013openalex-snapshot
Individuals and Relations
inferred← citeshttps://doi.org/10.1017/cbo9780511794797.014openalex-snapshot
Ontologies and Knowledge-Based Systems
inferred← citeshttps://doi.org/10.1017/cbo9780511794797.015openalex-snapshot
Relational Planning, Learning, and Probabilistic Reasoning
inferred← citeshttps://doi.org/10.1017/cbo9780511794797.017openalex-snapshot
Mathematical Preliminaries and Notation
inferred← citeshttps://doi.org/10.1017/s0269888997000015openalex-snapshot
Simplifying decision trees: A survey
inferred← citeshttps://doi.org/10.1017/s1351324900000218openalex-snapshot
A hierarchical Dirichlet language model
inferred← citeshttps://doi.org/10.1021/ci900046uopenalex-snapshot
Bayesian Model Averaging for Ligand Discovery
inferred← citeshttps://doi.org/10.1023/a:1007396710653openalex-snapshot
Predicting Nearly As Well As the Best Pruning of a Decision Tree
inferred← citeshttps://doi.org/10.1023/a:1007515423169openalex-snapshot
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
inferred← citeshttps://doi.org/10.1023/a:1007608224229openalex-snapshot
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms
inferred← citeshttps://doi.org/10.1023/a:1008931416845openalex-snapshot
Boosting with Bayesian stumps
inferred← citeshttps://doi.org/10.1023/a:1009730122752openalex-snapshot
Bayesian Networks for Data Mining
inferred← citeshttps://doi.org/10.1023/a:1009744630224openalex-snapshot
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
inferred← citeshttps://doi.org/10.1023/a:1018590219790openalex-snapshot
A fast splitting procedure for classification trees
inferred← citeshttps://doi.org/10.1023/a:1021840411064openalex-snapshot
PAC-Bayesian Stochastic Model Selection
inferred← citeshttps://doi.org/10.1023/b:apin.0000047384.85823.f6openalex-snapshot
Building a Medical Decision Support System for Colon Polyp Screening by Using Fuzzy Classification Trees
inferred← citeshttps://doi.org/10.1023/b:jims.0000026574.95637.36openalex-snapshot
Intelligent condition monitoring using fuzzy inductive learning
inferred← citeshttps://doi.org/10.1039/9781849737203-00172openalex-snapshot
High throughput technology: approaches of research in homogeneous and heterogeneous catalysis
inferred← citeshttps://doi.org/10.1039/b907946gopenalex-snapshot
Supervised learning with decision tree-based methods in computational and systems biology
inferred← citeshttps://doi.org/10.1039/b921393gopenalex-snapshot
Predictive modeling in homogeneous catalysis: a tutorial
inferred← citeshttps://doi.org/10.1061/(asce)0733-9399(2007)133:2(129)openalex-snapshot
Random Composites Characterization Using a Classifier Model
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