On the public citation graph
Evaluation of learning systems : An artificial data-based approach
0 signed · 16 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf00115008openalex-snapshot
Machine Learning as an Experimental Science
inferredcites →https://doi.org/10.1016/b978-1-55860-036-2.50052-7openalex-snapshot
AN EMPIRICAL ANALYSIS OF EBL APPROACHES FOR LEARNING PLAN SCHEMATA
inferredcites →https://doi.org/10.1016/b978-1-55860-036-2.50100-4openalex-snapshot
REDUCING REDUNDANT LEARNING
inferredcites →https://doi.org/10.1016/b978-1-55860-141-3.50022-5openalex-snapshot
NEWBOOLE: A Fast GBML System
inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot
Learning Internal Representations by Error Propagation
inferredcites →https://doi.org/10.2307/415721openalex-snapshot
Parallel Distributed Processing: Explorations in the microstructures of cognition. By David E. Rumelhart, James L. McClelland, and the PDP Research Group. Vol. I, Foundations; Vol. II, Psychological and biological models. Cambridge, MA: MIT Press, 1986. Pp. xx, 547; xii, 611.
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/bf00116899openalex-snapshot
Classification by Feature Partitioning
inferred← citeshttps://doi.org/10.1016/0004-3702(94)00094-8openalex-snapshot
Noise modelling and evaluating learning from examples
inferred← citeshttps://doi.org/10.1016/b978-1-55860-247-2.50030-9openalex-snapshot
Artificial Universes - Towards a Systematic Approach to Evaluating Algorithms which Learn from Examples
inferred← citeshttps://doi.org/10.1109/69.929904openalex-snapshot
Debiasing training data for inductive expert system construction
inferred← citeshttps://doi.org/10.1142/s0218194099000310openalex-snapshot
AN INVESTIGATION ON THE USE OF MACHINE LEARNED MODELS FOR ESTIMATING SOFTWARE CORRECTABILITY
inferred← citeshttps://doi.org/10.1287/ijoc.11.3.278openalex-snapshot
Optimizing Expert Systems: Heuristics for Efficiently Generating Low-Cost Information Acquisition Strategies
inferred← citeshttps://doi.org/10.1287/isre.11.2.137.11777openalex-snapshot
Mean-Risk Trade-Offs in Inductive Expert Systems
inferred← citeshttps://doi.org/10.1287/isre.6.4.328openalex-snapshot
Improving the Performance Stability of Inductive Expert Systems Under Input Noise
inferred← citeshttps://doi.org/10.1287/isre.8.1.51openalex-snapshot
Redesigning Case Retrieval to Reduce Information Acquisition Costs
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.