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A neuroidal architecture for cognitive computation

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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/bf00058680openalex-snapshot

Learning Decision Lists

inferredcites →https://doi.org/10.1007/bf00114802openalex-snapshot

Learning Conjunctive Concepts in Structural Domains

inferredcites →https://doi.org/10.1007/bf00116829openalex-snapshot

Learning From Noisy Examples

inferredcites →https://doi.org/10.1007/bf00117107openalex-snapshot

The Problem of Expensive Chunks and its Solution by Restricting Expressiveness

inferredcites →https://doi.org/10.1007/bfb0055091openalex-snapshot

A neuroidal architecture for cognitive computation

inferredcites →https://doi.org/10.1016/0004-3702(80)90011-9openalex-snapshot

Circumscription—A form of non-monotonic reasoning

inferredcites →https://doi.org/10.1016/0004-3702(80)90012-0openalex-snapshot

Non-monotonic logic I

inferredcites →https://doi.org/10.1016/0004-3702(88)90002-1openalex-snapshot

Quantifying inductive bias: AI learning algorithms and Valiant's learning framework

inferredcites →https://doi.org/10.1016/0004-3702(90)90019-vopenalex-snapshot

An analysis of first-order logics of probability

inferredcites →https://doi.org/10.1016/0004-3702(90)90059-9openalex-snapshot

Quantitative results concerning the utility of explanation-based learning

inferredcites →https://doi.org/10.1016/0031-3203(76)90020-0openalex-snapshot

The psychology of computer vision

inferredcites →https://doi.org/10.1016/0890-5401(89)90002-3openalex-snapshot

A general lower bound on the number of examples needed for learning

inferredcites →https://doi.org/10.1016/b978-0-08-094829-4.50022-2openalex-snapshot

From On-line to Batch Learning

inferredcites →https://doi.org/10.1016/b978-0-934613-03-3.50033-7openalex-snapshot

Some Philosophical Problems from the Standpoint of Artificial Intelligence

inferredcites →https://doi.org/10.1016/b978-1-4832-1446-7.50018-2openalex-snapshot

A FRAMEWORK FOR REPRESENTING KNOWLEDGE

inferredcites →https://doi.org/10.1016/s0022-0000(05)80062-5openalex-snapshot

Efficient distribution-free learning of probabilistic concepts

inferredcites →https://doi.org/10.1017/cbo9780511809477.009openalex-snapshot

Causal schemas in judgments under uncertainty

inferredcites →https://doi.org/10.1023/a:1007571119753openalex-snapshot

Learning to Take Actions

inferredcites →https://doi.org/10.1023/a:1007678005361openalex-snapshot

Projection Learning

inferredcites →https://doi.org/10.1023/a:1022653502461openalex-snapshot

Learning Boolean Functions in an Infinite Attribute Space

inferredcites →https://doi.org/10.1023/a:1022869011914openalex-snapshot

Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm

inferredcites →https://doi.org/10.1037/0033-295x.101.2.343openalex-snapshot

The magical number seven, plus or minus two: Some limits on our capacity for processing information.

inferredcites →https://doi.org/10.1037/h0043158openalex-snapshot

The magical number seven, plus or minus two: Some limits on our capacity for processing information.

inferredcites →https://doi.org/10.1093/mind/lix.236.433openalex-snapshot

I.—COMPUTING MACHINERY AND INTELLIGENCE

inferredcites →https://doi.org/10.1093/oso/9780195089264.001.0001openalex-snapshot

Circuits of the Mind

inferredcites →https://doi.org/10.1093/oso/9780198250791.003.0017openalex-snapshot

Computing Machinery and Intelligence (1950)

inferredcites →https://doi.org/10.1109/jcai15434.2009openalex-snapshot

2009 International Joint Conference on Artificial Intelligence

inferredcites →https://doi.org/10.1109/sfcs.1987.37openalex-snapshot

Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm

inferredcites →https://doi.org/10.1109/sfcs.1996.548492openalex-snapshot

A polynomial-time algorithm for learning noisy linear threshold functions

inferredcites →https://doi.org/10.1109/sfcs.1997.646140openalex-snapshot

Learning noisy perceptrons by a perceptron in polynomial time

inferredcites →https://doi.org/10.1137/0222052openalex-snapshot

Learning in the Presence of Malicious Errors

inferredcites →https://doi.org/10.1145/100216.100224openalex-snapshot

Learning boolean functions in an infinite attribute space

inferredcites →https://doi.org/10.1145/167088.167198openalex-snapshot

How to use expert advice

inferredcites →https://doi.org/10.1145/167088.167200openalex-snapshot

Efficient noise-tolerant learning from statistical queries

inferredcites →https://doi.org/10.1145/180139.181176openalex-snapshot

Learning linear threshold functions in the presence of classification noise

inferredcites →https://doi.org/10.1145/181668.181671openalex-snapshot

Inductive logic programming

inferredcites →https://doi.org/10.1145/1968.1972openalex-snapshot

A theory of the learnable

inferredcites →https://doi.org/10.1145/225298.225299openalex-snapshot

Rationality

inferredcites →https://doi.org/10.1145/225298.225333openalex-snapshot

The perceptron algorithm vs. Winnow

inferredcites →https://doi.org/10.1145/225298.225335openalex-snapshot

Learning to reason with a restricted view

inferredcites →https://doi.org/10.1145/279943.279999openalex-snapshot

Projection learning

inferredcites →https://doi.org/10.1145/293347.293351openalex-snapshot

Efficient noise-tolerant learning from statistical queries

inferredcites →https://doi.org/10.1145/322290.322293openalex-snapshot

Nonmonotonic Logic II

inferredcites →https://doi.org/10.1145/3749740.3749746openalex-snapshot

A Theory of the Learnable

inferredcites →https://doi.org/10.1145/48014.63140openalex-snapshot

Computational limitations on learning from examples

inferredcites →https://doi.org/10.1145/79173.79176openalex-snapshot

Cyc: toward programs with common sense

inferredcites →https://doi.org/10.2307/2063712openalex-snapshot

Human Problem Solving.

inferredcites →https://doi.org/10.2307/2268088openalex-snapshot

A. M. Turing. Solvable and unsolvable problems. Science news (Penguin books, Melbourne–London–Baltimore), no. 31 (1954), pp. 7–23.

inferredcites →https://doi.org/10.2307/2312103openalex-snapshot

Principles of Neurodynamics.

inferredcites →https://doi.org/10.4324/9780203114261-10openalex-snapshot

Artificial Intelligence

inferredcites →https://doi.org/10.4324/9781315725765openalex-snapshot

Progress in Social Psychology

inferredcites →https://doi.org/10.4324/9781315799438openalex-snapshot

The Architecture of Cognition

inferredcites →https://doi.org/10.48550/arxiv.cmp-lg/9607024openalex-snapshot

Applying Winnow to Context-Sensitive Spelling Correction

inferredcites →https://doi.org/10.5555/93335.93365openalex-snapshot

From on-line to batch learning

inferredcites →https://doi.org/10.5860/choice.28-5376openalex-snapshot

Unified theories of cognition

inferredcites →https://doi.org/10.5860/choice.33-1529openalex-snapshot

Circuits of the mind

inferredcites →https://doi.org/10.7551/mitpress/3897.001.0001openalex-snapshot

An Introduction to Computational Learning Theory

inferredcites →https://doi.org/10.7551/mitpress/5605.001.0001openalex-snapshot

Production System Models of Learning and Development

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-1-4615-0013-1_16openalex-snapshot

The Random Projection Method

inferred← citeshttps://doi.org/10.1007/s10994-006-6265-7openalex-snapshot

An algorithmic theory of learning: Robust concepts and random projection

inferred← citeshttps://doi.org/10.1016/s0004-3702(00)00002-3openalex-snapshot

Robust logics

inferred← citeshttps://doi.org/10.1023/a:1007678005361openalex-snapshot

Projection Learning

inferred← citeshttps://doi.org/10.1109/sffcs.1999.814637openalex-snapshot

An algorithmic theory of learning: robust concepts and random projection

inferred← citeshttps://doi.org/10.1145/279943.279999openalex-snapshot

Projection learning

inferred← citeshttps://doi.org/10.1145/301250.301425openalex-snapshot

Robust logics

inferred← citeshttps://doi.org/10.1609/aaai.v30i1.10099openalex-snapshot

Learning Abductive Reasoning Using Random Examples

inferred← citeshttps://doi.org/10.1609/aaai.v31i1.10700openalex-snapshot

An Improved Algorithm for Learning to Perform Exception-Tolerant Abduction

inferred← citeshttps://doi.org/10.1609/aaai.v34i04.5864openalex-snapshot

More Accurate Learning of k-DNF Reference Classes

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