Everything about Learning rule
Artificial neural network algorithm
An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network. It is done by updating the weight and bias levels of a network when it is simulated in a specific data environment. A learning rule may accept existing conditions of the network, and will compare the expected result and actual result of the network to give new and improved values for the weights and biases. Depending on the complexity of the model being simulated, the learning rule of the network can be as simple as an XOR gate or mean squared error, or as complex as the result of a system of differential equations. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Learning ruleA 5-minute orientation
- Skim a recent paper: Fuzzy Rule-Based Classification Method for Incremental Rule LearningInfluential work since 2022
- Pick up a classic book: Kokuritsu Kokkai Toshokan shozō Meijiki kankō tosho maikuro-ban shūseiThe most-reprinted book on the subject
- Read the landmark paper: Predictive learning via rule ensemblesCited 993 times
- Go sideways: ADALINEA closely related topic
- Go sideways: Oja's ruleA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Predictive learning via rule ensembles
Separate-and-Conquer Rule Learning
BEYOND EXPLICIT RULE LEARNING
A new adaptive learning rule
Infant Rule Learning Facilitated by Speech
Rule Learning by Rats
Pruning Algorithms for Rule Learning
A "Thermal" Perceptron Learning Rule
🆕 Influential since 2022
Fuzzy Rule-Based Classification Method for Incremental Rule Learning
Federated Fuzzy Neural Network With Evolutionary Rule Learning
Revolt and Rule: Learning about Governance from Rebel Groups
Rule mining over knowledge graphs via reinforcement learning
A cognitive category-learning model of rule abstraction, attention learning, and contextual modulation.
📚 Books worth reading
- 📕Kokuritsu Kokkai Toshokan shozō Meijiki kankō tosho maikuro-ban shūseiKokuritsu Kokkai Toshokan (Japan) · 1991
Do Unto OttersLaurie Keller · 2007
Rule Based Systems for Big DataHan Liu, Alexander Gegov · 2015- 📕Dong Ya chuan tong jiao yu yu xue li xue guiMingshi Gao · 2008
Neuroscience of rule-guided behavior · 2008
Neuroscience of rule-guided behaviorS Bunge · 2007- 📕Naxçıvanı öyränän elmi cämiyyätFärman Xälilov · 2005
La poésie scaldiqueRégis Boyer · 1990
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- ADALINE ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device…
- Oja's rule Oja's learning rule, or simply Oja's rule, named after Finnish computer scientist Erkki Oja (Finnish pronunciation: [ˈojɑ], AW-yuh), is a model of how…
- Generalized Hebbian algorithm The generalized Hebbian algorithm, also known in the literature as Sanger's rule, is a linear feedforward neural network for unsupervised learning with…
- Artificial neuron An artificial neuron is a mathematical function conceived as a model of a biological neuron in a neural network. The artificial neuron is the elementary…
- Multilayer perceptron In deep learning, a multilayer perceptron (MLP) is a kind of modern feedforward neural network consisting of fully connected neurons with nonlinear activation…
- Feedforward neural network A feedforward neural network is an artificial neural network in which information flows in a single direction – inputs are multiplied by weights to obtain…
- Hopfield network A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory…
- Hebbian theory Hebbian theory is a neuropsychological theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulatio…