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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.
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The main idea of ESNs is tied to liquid state machines, which were independently and simultaneously developed with ESNs by Wolfgang Maass. [6] They, ESNs and the newly researched backpropagation decorrelation learning rule for RNNs [ 7 ] are more and more summarized under the name Reservoir Computing.
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 learning machines (ELM) is a special case of single hidden layer feed-forward neural networks (SLFNs) wherein the input weights and the hidden node biases can be chosen at random. Many variants and developments are made to the ELM for multiclass classification.
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 .
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 ...
There's actually quite a bit of controversy surrounding the "extreme learning machine", but I'm having trouble finding properly published sources about it. There's a Facebook post by Yann LeCun which points out that this is really a relabeling of the original (1958) perceptron algorithm/hardware, but even though LeCun is an expert on neural nets, I'm hesitant to cite a Facebook post.