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Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries

https://doi.org/10.1007/bf00993305
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What this work says

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

inferredcites →https://doi.org/10.1007/3-540-56483-7_21openalex-snapshot
Cryptographic limitations on learning Boolean formulae and finite automata
inferredcites →https://doi.org/10.1007/bf00114777openalex-snapshot
Complexity Results on Learning by Neural Nets
inferredcites →https://doi.org/10.1007/bf02579150openalex-snapshot
A new polynomial-time algorithm for linear programming
inferredcites →https://doi.org/10.1016/0006-8993(90)91657-3openalex-snapshot
Cyclic AMP-induced shape changes of astrocytes are accompanied by rapid depolymerization of actin
inferredcites →https://doi.org/10.1016/0885-064x(88)90019-2openalex-snapshot
On the complexity of loading shallow neural networks
inferredcites →https://doi.org/10.1016/0885-064x(90)90012-3openalex-snapshot
On learning a union of half spaces
inferredcites →https://doi.org/10.1016/b978-1-55860-146-8.50034-5openalex-snapshot
Exact Identification of Circuits Using Fixed Points of Amplification Functions
inferredcites →https://doi.org/10.1016/b978-1-55860-213-7.50021-3openalex-snapshot
Learning Probabilistic Readonce Formulas on Product Distributions
inferredcites →https://doi.org/10.1103/physrevlett.65.2312openalex-snapshot
Statistical mechanics of a multilayered neural network
inferredcites →https://doi.org/10.1109/72.80287openalex-snapshot
Neural net algorithms that learn in polynomial time from examples and queries
inferredcites →https://doi.org/10.1109/fscs.1990.89538openalex-snapshot
Exact identification of circuits using fixed points of amplification functions
inferredcites →https://doi.org/10.1145/103418.103420openalex-snapshot
When won't membership queries help?
inferredcites →https://doi.org/10.1145/129712.129747openalex-snapshot
Learning arithmetic read-once formulas
inferredcites →https://doi.org/10.1145/130385.130386openalex-snapshot
Learning Boolean read-once formulas with arbitrary symmetric and constant fan-in gates
inferredcites →https://doi.org/10.1145/138027.138061openalex-snapshot
Learning read-once formulas with queries
inferredcites →https://doi.org/10.1145/167088.167197openalex-snapshot
Cryptographic hardness of distribution-specific learning
inferredcites →https://doi.org/10.1145/174644.174647openalex-snapshot
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata
inferredcites →https://doi.org/10.1145/1968.1972openalex-snapshot
A theory of the learnable
inferredcites →https://doi.org/10.1145/28395.28426openalex-snapshot
On the learnability of Boolean formulae
inferredcites →https://doi.org/10.1145/3749740.3749746openalex-snapshot
A Theory of the Learnable
inferredcites →https://doi.org/10.1145/73007.73049openalex-snapshot
Crytographic limitations on learning Boolean formulae and finite automata
inferredcites →https://doi.org/10.1145/76359.76371openalex-snapshot
Learnability and the Vapnik-Chervonenkis dimension
inferredcites →https://doi.org/10.1162/neco.1989.1.1.151openalex-snapshot
What Size Net Gives Valid Generalization?
inferredcites →https://doi.org/10.1162/neco.1990.2.4.510openalex-snapshot
A Polynomial Time Algorithm That Learns Two Hidden Unit Nets
inferredcites →https://doi.org/10.1209/0295-5075/14/2/003openalex-snapshot
Storage Capacity of a Multilayer Neural Network with Binary Weights
inferredcites →https://doi.org/10.5555/114836.114854openalex-snapshot
Learning probabilistic read-once formulas on product distributions
inferredcites →https://doi.org/10.7551/mitpress/11474.003.0014openalex-snapshot
Neural Information Processing Systems

What points at this work

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

inferred← citeshttps://doi.org/10.1006/jcss.1995.1042openalex-snapshot
Learning Boolean Read-Once Formulas over Generalized Bases
inferred← citeshttps://doi.org/10.1007/3-540-44673-7_4openalex-snapshot
Function Decomposition in Machine Learning
inferred← citeshttps://doi.org/10.1007/3-540-49730-7_26openalex-snapshot
On the Sample Complexity for Neural Trees
inferred← citeshttps://doi.org/10.1007/3-540-60454-5_40openalex-snapshot
Complexity of network training for classes of Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-031-91107-1_13openalex-snapshot
Polynomial Time Cryptanalytic Extraction of Deep Neural Networks in the Hard-Label Setting
inferred← citeshttps://doi.org/10.1016/0893-6080(95)00009-7openalex-snapshot
On learning μ-perceptron networks on the uniform distribution
inferred← citeshttps://doi.org/10.1016/s0004-3702(99)00008-9openalex-snapshot
Learning by discovering concept hierarchies
inferred← citeshttps://doi.org/10.1023/a:1007609822199openalex-snapshot
On the Sample Complexity for Nonoverlapping Neural Networks
inferred← citeshttps://doi.org/10.1088/0954-898x/4/1/005openalex-snapshot
On learning simple neural concepts: from halfspace intersections to neural decision lists
inferred← citeshttps://doi.org/10.1109/5254.671090openalex-snapshot
Feature transformation by function decomposition
inferred← citeshttps://doi.org/10.1109/cec.2013.6557932openalex-snapshot
Modeling hierarchy using symbolic regression
inferred← citeshttps://doi.org/10.1145/130385.130386openalex-snapshot
Learning Boolean read-once formulas with arbitrary symmetric and constant fan-in gates
inferred← citeshttps://doi.org/10.1145/168304.168323openalex-snapshot
Average case analysis of the clipped Hebb rule for nonoverlapping perception networks
inferred← citeshttps://doi.org/10.1162/089976600300014999openalex-snapshot
Generalization and Selection of Examples in Feedforward Neural Networks
inferred← citeshttps://doi.org/10.1162/08997660252741121openalex-snapshot
On the Complexity of Computing and Learning with Multiplicative Neural Networks
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