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Once a matrix or a high-dimensional vector is transferred to a sparse space, different recovery algorithms like basis pursuit, CoSaMP, [1] or fast non-iterative algorithms [2] can be used to recover the signal. One of the key principles of dictionary learning is that the dictionary has to be inferred from the input data.
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision , where a small portion of the data is tagged, and self-supervision .
In addition to the supervised learning setting, sample complexity is relevant to semi-supervised learning problems including active learning, [7] where the algorithm can ask for labels to specifically chosen inputs in order to reduce the cost of obtaining many labels.
C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform. Smalltalk has been used extensively for simulations, neural networks, machine learning, and genetic algorithms.
Co-training is a machine learning algorithm used when there are only small amounts of labeled data and large amounts of unlabeled data. One of its uses is in text mining for search engines. It was introduced by Avrim Blum and Tom Mitchell in 1998.
Sparse identification of nonlinear dynamics (SINDy) is a data-driven algorithm for obtaining dynamical systems from data. [1] Given a series of snapshots of a dynamical system and its corresponding time derivatives, SINDy performs a sparsity-promoting regression (such as LASSO and spare Bayesian inference [2]) on a library of nonlinear candidate functions of the snapshots against the ...
Stability, also known as algorithmic stability, is a notion in computational learning theory of how a machine learning algorithm output is changed with small perturbations to its inputs. A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly.
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