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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 (not just the weights connecting inputs to hidden nodes) need to be tuned.
It runs on a single machine, as well as the distributed processing frameworks Apache Hadoop, Apache Spark, Apache Flink, and Dask. [ 9 ] [ 10 ] XGBoost gained much popularity and attention in the mid-2010s as the algorithm of choice for many winning teams of machine learning competitions .
Learning dynamical processes: signal treatment in engineering and telecommunications, vibration analysis, seismology, and control of engines and generators. Signal forecasting and generation: text, music, electric signals, chaotic signals.
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Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional boosting. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple ...
Extreme Machines was a documentary series created by Pioneer Productions for The Learning Channel and Discovery Channel. The series focused mainly on machines although in some episodes of Season 4 and Season 5, it also looked at disasters involving them. [1] The series was largely narrated by William Hootkins. The show made also made use of ...
Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units) A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
At the other extreme are models that can be reproduced only by exactly duplicating the original modeler's entire setup, making reuse or scientific reproduction difficult. [11] It may be possible to reconstruct details of individual training instances from an overfitted machine learning model's training set.