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Scaling Up Inductive Learning with Massive Parallelism
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4613-1557-5_6openalex-snapshot
Incremental Batch Learning
inferredcites →https://doi.org/10.1007/bf00993472openalex-snapshot
Evaluation and Selection of Biases in Machine Learning
inferredcites →https://doi.org/10.1007/bf00993474openalex-snapshot
Inductive Policy: The Pragmatics of Bias Selection
inferredcites →https://doi.org/10.1007/bf01389000openalex-snapshot
Parallel depth first search. Part I. Implementation
inferredcites →https://doi.org/10.1007/bf01389001openalex-snapshot
Parallel depth first search. Part II. Analysis
inferredcites →https://doi.org/10.1007/bfb0017012openalex-snapshot
On changing continuous attributes into ordered discrete attributes
inferredcites →https://doi.org/10.1016/0004-3702(93)90002-sopenalex-snapshot
Depth-first heuristic search on a SIMD machine
inferredcites →https://doi.org/10.1016/0004-3702(93)90003-topenalex-snapshot
A SIMD approach to parallel heuristic search
inferredcites →https://doi.org/10.1016/b978-0-12-737550-2.50019-1openalex-snapshot
MODEL-DIRECTED LEARNING OF PRODUCTION RULES11This work was supported by the Advanced Research Projects Agency under contract DAHC 15-73-C-0435, and by the National Institutes of Health under grant RR 00612–07.
inferredcites →https://doi.org/10.1016/b978-1-55860-036-2.50046-1openalex-snapshot
AN OUNCE OF KNOWLEDGE IS WORTH A TON OF DATA: Quantitative Studies of the Trade-Off between Expertise and Data based on Statistically Well-Founded Empirical Induction
inferredcites →https://doi.org/10.1016/b978-1-55860-036-2.50093-xopenalex-snapshot
INCREMENTAL BATCH LEARNING
inferredcites →https://doi.org/10.1016/b978-1-55860-200-7.50121-5openalex-snapshot
Mega induction: a Test Flight
inferredcites →https://doi.org/10.1016/b978-1-55860-247-2.50012-7openalex-snapshot
Peepholing: choosing attributes efficiently for megainduction
inferredcites →https://doi.org/10.1016/b978-1-55860-307-3.50017-4openalex-snapshot
Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network
inferredcites →https://doi.org/10.1016/c2013-0-11671-6openalex-snapshot
Pattern-Directed Inference Systems
inferredcites →https://doi.org/10.1023/a:1022631118932openalex-snapshot
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
inferredcites →https://doi.org/10.1023/a:1022699900025openalex-snapshot
Incremental Induction of Decision Trees
inferredcites →https://doi.org/10.1109/mc.1987.1663360openalex-snapshot
Initial Performance of the DADO2 Prototype
inferredcites →https://doi.org/10.1109/spdp.1990.143582openalex-snapshot
Accelerated learning on the connection machine
inferredcites →https://doi.org/10.1109/tai.1990.130305openalex-snapshot
RL4: a tool for knowledge-based induction
inferredcites →https://doi.org/10.1111/an.1999.40.2.6openalex-snapshot
The Massachusetts Institute of Technology
inferredcites →https://doi.org/10.1142/s0218213093000102openalex-snapshot
MASSIVELY PARALLEL IDA* SEARCH
inferredcites →https://doi.org/10.1145/1045343.1045371openalex-snapshot
Model-directed learning of production rules
inferredcites →https://doi.org/10.1184/r1/6550169openalex-snapshot
A distributed problem-solving approach to inductive learning
inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot
Learning Internal Representations by Error Propagation
inferredcites →https://doi.org/10.7551/mitpress/5236.001.0001openalex-snapshot
Parallel Distributed Processing
inferredcites →https://doi.org/10.7916/d8sq97cnopenalex-snapshot
DADO: A Tree-Structured Machine Architecture for Production Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Genetic Algorithm-Based Solution for the Problem of Small Disjuncts
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The Need for Low Bias Algorithms in Classification Learning from Large Data Sets
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Parallel and Distributed Data Mining: An Introduction
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Adaptation and learning in multi-agent systems: Some remarks and a bibliography
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inferred← citeshttps://doi.org/10.1007/978-3-540-77467-9_6openalex-snapshot
Classification and Survival Analysis Using Multi-objective Evolutionary Algorithms
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Inductive Policy: The Pragmatics of Bias Selection
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inferred← citeshttps://doi.org/10.1007/s10994-008-5083-5openalex-snapshot
Discretization for naive-Bayes learning: managing discretization bias and variance
inferred← citeshttps://doi.org/10.1016/0027-5107(94)00202-gopenalex-snapshot
Learning rules to predict rodent carcinogenicity of non-genotoxic chemicals
inferred← citeshttps://doi.org/10.1016/j.biombioe.2025.108265openalex-snapshot
A hybrid XGBoost-based model approach to evaluate the effect of biomass fly ash in vegetable and fruit waste composting
inferred← citeshttps://doi.org/10.1016/j.biosystems.2005.02.003openalex-snapshot
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Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
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Guest Editors' Introduction: On Applied Research in Machine Learning
inferred← citeshttps://doi.org/10.1023/a:1007460424845openalex-snapshot
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
inferred← citeshttps://doi.org/10.1023/a:1009876119989openalex-snapshot
A Survey of Methods for Scaling Up Inductive Algorithms
inferred← citeshttps://doi.org/10.1089/big.2013.0037openalex-snapshot
Predictive Modeling With Big Data: Is Bigger Really Better ?
inferred← citeshttps://doi.org/10.1109/fskd.2011.6019750openalex-snapshot
Bisecting data partitioning methods for Min-Max Modular Support Vector Machine
inferred← citeshttps://doi.org/10.1109/iccsit.2009.5234490openalex-snapshot
Using parallel partitioning strategy to create diversity for ensemble learning
inferred← citeshttps://doi.org/10.1109/icmlc.2007.4370776openalex-snapshot
On Combining Distributed SVMs by Simple Bayesian Formalism Rules
inferred← citeshttps://doi.org/10.1109/iv.2013.98openalex-snapshot
Visualisation of Association Rules Based on a Molecular Representation
inferred← citeshttps://doi.org/10.1145/349093.349109openalex-snapshot
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inferred← citeshttps://doi.org/10.1145/944618.944630openalex-snapshot
Relational concept learning by cooperative evolution
inferred← citeshttps://doi.org/10.11606/d.55.2002.tde-04072002-144610openalex-snapshot
Análise do conhecimento extraído de classificadores simbólicos utilizando medidas de avaliação e de interessabilidade.
inferred← citeshttps://doi.org/10.11606/d.55.2018.tde-09032018-135652openalex-snapshot
Avaliação do Conhecimento Adquirido por Algoritmos de Aprendizado de Máquina Utilizando Exemplos
inferred← citeshttps://doi.org/10.1289/ehp.96104s51059openalex-snapshot
Carcinogenicity Predictions for a Group of 30 Chemicals Undergoing Rodent Cancer Bioassays Based on Rules Derived from Subchronic Organ Toxicities
inferred← citeshttps://doi.org/10.17638/03186718openalex-snapshot
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inferred← citeshttps://doi.org/10.3233/ida-2006-10604openalex-snapshot
A comprehensive survey of numeric and symbolic outlier mining techniques
inferred← citeshttps://doi.org/10.48550/arxiv.1302.1529openalex-snapshot
Exploring Parallelism in Learning Belief Networks
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