Concept acquisition through representational adjustment

By Jeffrey C. Schlimmer. 1987

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Citations in the corpus

Missing Data Imputation for Supervised Learning (2016, methodology): “The CVRs dataset donor claims to achieve a 5–10% error rate using an incremental decision tree algorithm called STAGGER, although it is unknown to the authors what train-test split is used or how missing values are handled (Schlimmer 1987; Schlimmer and Granger 1986).”
Learning Optimized Risk Scores (2016, methodology): “rrested within 3 years of release bank Moro et al. (2014) predict if person signs up for bank account after marketing call cancer Mangasarian et al. (1995) detect breast cancer from a biopsy mushroom Schlimmer (1987) predict if a mushroom is poisonous spambase Cranor and LaMacchia (1998) predict if ”
Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “survival after breast cancer surgery heart Detrano et al. (1989) 303 32 identify patients a high risk of heart disease mammo Elter et al. (2007) 961 12 detect breast cancer using a mammogram mushroom Schlimmer (1987) 8124 113 predict if a mushroom is poisonous spambase Cranor and LaMacchia (1998) 46”
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts (2007, methodology): “In this evaluation, we compared the performance of DWM-NB and DWM-ITI 1. to best-and worst-case base learners, 2. to our implementation of STAGGER (Schlimmer, 1987), 3. to AQ-PM (Maloof and Michalski, 2000), AQ11-PM (Maloof and Michalski, 2004), and AQ11- PM+WAH (Maloof, 2003), and 4. to Blum's…”
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets (1993, background): “MU, VO(V1): Schlimmer (1987) . possible with relatively simple rules.”
Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta (1992, methodology): “The capabilities of the IDBD algorithm were assessed using a series of tracking tasks—supervised-learning or concept-learning tasks in which the target concept drifts over time and has to be tracked (cf. Schlimmer 1987).”
Symbolic Interpretation of Artificial Neural Networks (1999, methodology): “The input features are: Other popular data sets that have been used as benchmarks for rule-extraction approaches are the Monk [43], Mushroom [32], and the DNA promoter [47] datasets.”
Feature Generation Using General Constructor Functions (2002, methodology): “IB3-CI integrates instancebased learning performed by the IB3 (Aha, Kibler, & Albert, 1991) algorithm with incremental feature construction performed by the STAGGER (Schlimner, 1987) algorithm.”
ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network (1991, methodology): “Each vector a characterizes observable features of a mushroom as a binary vector, and each mushroom is classified as edible or poisonous (Schlimmer, 1987a).”