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Toward a unified science of machine learning

https://doi.org/10.1007/bf00116834
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4613-2283-2openalex-snapshot
Machine Learning of Inductive Bias
inferredcites →https://doi.org/10.1007/bf00114265openalex-snapshot
Knowledge Acquisition Via Incremental Conceptual Clustering
inferredcites →https://doi.org/10.1007/bf00115008openalex-snapshot
Machine Learning as an Experimental Science
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/b978-0-934613-41-5.50006-4openalex-snapshot
Learning Representative Exemplars of Concepts: An Initial Case Study
inferredcites →https://doi.org/10.1016/b978-0-934613-41-5.50012-xopenalex-snapshot
Incremental Adjustment of Representations for Learning
inferredcites →https://doi.org/10.1016/b978-0-934613-41-5.50027-1openalex-snapshot
Inducing Causal and Social Theories: A Prerequisite for Explanation-based Learning
inferredcites →https://doi.org/10.1016/b978-0-934613-64-4.50013-xopenalex-snapshot
Trading Off Simplicity and Coverage in Incremental Concept Learning
inferredcites →https://doi.org/10.1016/s0364-0213(83)80001-9openalex-snapshot
Maintaining Organization in a Dynamic Long‐Term Memory*
inferredcites →https://doi.org/10.1080/09540098908915648openalex-snapshot
Perceptron Trees: A Case Study in Hybrid Concept Representations
inferredcites →https://doi.org/10.1145/1968.1972openalex-snapshot
A theory of the learnable
inferredcites →https://doi.org/10.1207/s15516709cog1301_1openalex-snapshot
The Logic of Plausible Reasoning: A Core Theory
inferredcites →https://doi.org/10.4324/9780203728178openalex-snapshot
Cognitive Skills and Their Acquisition
inferredcites →https://doi.org/10.7916/d8pv6tfwopenalex-snapshot
Approximate Theory Formation: An Explanation-Based Approach

What points at this work

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

inferred← citeshttps://doi.org/10.1007/bf00994111openalex-snapshot
A Framework for Average Case Analysis of Conjunctive Learning Algorithms
inferred← citeshttps://doi.org/10.1016/0952-1976(96)00004-8openalex-snapshot
Extraction of comprehensive symbolic rules from a multi-layer perceptron
inferred← citeshttps://doi.org/10.1016/b978-1-4832-0773-5.50012-2openalex-snapshot
Theory-Guided Concept Formation
inferred← citeshttps://doi.org/10.1016/b978-1-55860-141-3.50044-4openalex-snapshot
Average Case Analysis of Conjunctive Learning Algorithms
inferred← citeshttps://doi.org/10.1016/j.patrec.2015.04.014openalex-snapshot
On unifiers, diversifiers, and the nature of pattern recognition
inferred← citeshttps://doi.org/10.1017/s0140525x00079760openalex-snapshot
What connectionist models learn: Learning and representation in connectionist networks
inferred← citeshttps://doi.org/10.1017/s0140525x00079772openalex-snapshot
Relatively local neurons in a distributed representation: A neurophysiological perspective
inferred← citeshttps://doi.org/10.1017/s0140525x00079784openalex-snapshot
What connectionists learn: Comparisons of model and neural nets
inferred← citeshttps://doi.org/10.1017/s0140525x00079796openalex-snapshot
Representational systems and symbolic systems
inferred← citeshttps://doi.org/10.1017/s0140525x00079802openalex-snapshot
Connectionism and classical computation
inferred← citeshttps://doi.org/10.1017/s0140525x00079814openalex-snapshot
Are connectionist models just statistical pattern classifiers?
inferred← citeshttps://doi.org/10.1017/s0140525x00079826openalex-snapshot
Expose hidden assumptions in network theory
inferred← citeshttps://doi.org/10.1017/s0140525x00079838openalex-snapshot
But what is the substance of connectionist representation?
inferred← citeshttps://doi.org/10.1017/s0140525x0007984xopenalex-snapshot
A non-empiricist perspective on learning in layered networks
inferred← citeshttps://doi.org/10.1017/s0140525x00079851openalex-snapshot
How connectionist models learn: The course of learning in connectionist networks
inferred← citeshttps://doi.org/10.1017/s0140525x00079863openalex-snapshot
What can psychologists learn from hidden-unit nets?
inferred← citeshttps://doi.org/10.1017/s0140525x00079875openalex-snapshot
Approaches to learning and representation
inferred← citeshttps://doi.org/10.1017/s0140525x00079887openalex-snapshot
On learnability, empirical foundations, and naturalness
inferred← citeshttps://doi.org/10.1017/s0140525x00079899openalex-snapshot
Toward a unification of conditioning and cognition in animal learning
inferred← citeshttps://doi.org/10.1017/s0140525x00079905openalex-snapshot
Keeping representations at bay
inferred← citeshttps://doi.org/10.1017/s0140525x00079917openalex-snapshot
Learning from learned networks
inferred← citeshttps://doi.org/10.1017/s0140525x00079929openalex-snapshot
Realistic neural nets need to learn iconic representations
inferred← citeshttps://doi.org/10.1017/s0140525x00079930openalex-snapshot
The analysis of the learning needs to be deeper
inferred← citeshttps://doi.org/10.1017/s0140525x00079942openalex-snapshot
There is more to learning then meeth the eye (or ear)
inferred← citeshttps://doi.org/10.1017/s0140525x00079954openalex-snapshot
Problems of extension, representation, and computational irreducibility
inferred← citeshttps://doi.org/10.1017/s0140525x00079966openalex-snapshot
Connectionist models: Too little too soon?
inferred← citeshttps://doi.org/10.1017/s0140525x00079978openalex-snapshot
Advances in neural network theory
inferred← citeshttps://doi.org/10.1017/s0140525x0007998xopenalex-snapshot
Connectionist models learn what?
inferred← citeshttps://doi.org/10.1017/s0140525x00079991openalex-snapshot
Connectionist learning and the challenge of real environments
inferred← citeshttps://doi.org/10.1017/s0140525x00080006openalex-snapshot
Learning and representation: Tensions at the interface
inferred← citeshttps://doi.org/10.1109/inbs.1995.404278openalex-snapshot
Discovery of comprehensible symbolic rules in a neural network
inferred← citeshttps://doi.org/10.1162/99608f92.1d34757bopenalex-snapshot
Toward a 'Standard Model' of Machine Learning
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