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Sparse dictionary learning has been successfully applied to various image, video and audio processing tasks as well as to texture synthesis [15] and unsupervised clustering. [16] In evaluations with the Bag-of-Words model, [17] [18] sparse coding was found empirically to outperform other coding approaches on the object category recognition tasks.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
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 is mostly used for numerical analysis, computational science, and machine learning. [6] 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.
In machine learning, grokking, or delayed generalization, is a transition to generalization that occurs many training iterations after the interpolation threshold, after many iterations of seemingly little progress, as opposed to the usual process where generalization occurs slowly and progressively once the interpolation threshold has been ...
LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and ...
Pages in category "Machine learning algorithms" ... Multi expression programming; Multiple kernel learning; N. ... a non-profit organization.
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 ...