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Everything about Extreme learning machine
Type of artificial neural network
Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes need to be tuned. These hidden nodes can be randomly assigned and never updated, or can be inherited from their ancestors without being changed. In most cases, the output weights of hidden nodes are usually learned in a single step, which essentially amounts to learning a linear model. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Extreme learning machineA 5-minute orientation
- Skim a recent paper: Extreme learning machine based transfer learning for aero engine fault diagnosisInfluential work since 2022
- Pick up a classic book: Harcourt ScienceThe most-reprinted book on the subject
- Read the landmark paper: Extreme learning machine: Theory and applicationsCited 11,953 times
- Go sideways: Echo state networkA closely related topic
- Go sideways: Types of artificial neural networksA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Extreme learning machine: Theory and applications
Convex incremental extreme learning machine
Evolutionary extreme learning machine
Weighted extreme learning machine for imbalance learning
A review on extreme learning machine
Extreme learning machine and its applications
Voting based extreme learning machine
Fully complex extreme learning machine
🆕 Influential since 2022
Extreme learning machine based transfer learning for aero engine fault diagnosis
Multi-Swarm Algorithm for Extreme Learning Machine Optimization
Machine learning–based extreme event attribution
Wavelet extreme learning machine and deep learning for data classification
Embedding metric learning into an extreme learning machine for scene recognition
📚 Books worth reading
Harcourt ScienceHoughton Mifflin Harcourt Staff · 2008
Science Leveled ReadersHoughton Mifflin Harcourt Staff · 2011
Intelligence BulletinUnited States. War Department. Military Intelligence Service · 1942
Advances in Computational IntelligenceIgnacio Rojas · 2013- 📕Artificial intelligenceStephanie Sammartino McPherson · 2017
ContextCory Doctorow, Tim O'Reilly · 2011
Digital planetGeorge Beekman, Ben Beekman · 2011
PTAexamScott M. Giles · 2010
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Echo state network An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically…
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- Entropy estimation In various science/engineering applications, such as independent component analysis, image analysis, genetic analysis, speech recognition, manifold learning…
- Universal approximation theorem In the field of machine learning, the universal approximation theorems (UATs) state that neural networks with a certain structure can, in principle, approximate…
- 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…
- Neural network (machine learning) In machine learning, a neural network (NN) or artificial neural network (ANN) is a computational model inspired by the structure and functions of biological…
- Probabilistic neural network A probabilistic neural network (PNN) is a feedforward neural network, which is widely used in classification and pattern recognition problems. In the PNN…