Randomization tests
Multivariate analysis of variance for behavioural scientists
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Decision trees and multi-valued attributes
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Inferring decision trees using the minimum description length principle
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Learnability and the Vapnik-Chervonenkis dimension
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Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Statistical significance in inductive learning
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Neural networks and the bias/variance dilemma
Neural Computation
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Induction with randomization testing: decision-oriented analysis of large data sets
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Superstitious learning and induction
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Empirical methods for artificial intelligence
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Overfitting and undercomputing in machine learning
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A Statistical Theory for Quantitative Association Rules
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A single mechanism is responsible for three pathologies ofinduction algorithms: attribute selection errors, overfitting, andoversearching. In each pathology, induction algorithms comparemultiple items based on scores from an evaluation function andselect the item with the maximum score. We call this amultiple comparison procedure (MCP). We analyze thestatistical properties of MCPs and show how failure to adjustfor these properties leads to the pathologies. We also discussapproaches that can control pathological behavior, includingBonferroni adjustment, randomization testing, andcross-validation.