MLwebhttp://mloss.orgUpdates and additions to MLwebenFri, 23 Feb 2018 15:40:27 -0000MLweb 1.2<html><p>MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side, i.e., in the browser. </p> <p>It includes the following. </p> <h2>LALOLib: a javascript library to enable and ease scientific computing within web pages</h2> <p>LALOLib provides functions for </p> <ul> <li> linear algebra: basic vector and matrix operations, linear system solvers, matrix factorizations (QR, Cholesky), eigendecomposition, singular value decomposition, conjugate gradient sparse linear system solver, complex numbers/matrices, discrete Fourier transform... ), </li> <li> statistics: sampling from and estimating standard distributions, </li> <li> optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming. </li> </ul> <p>Documentation is available at <a href=""></a> </p> <p>See also the benchmark at <a href=""></a> </p> <h2>ML.js: a javascript library for machine learning</h2> <p>In addition to all the functions of LALOLib, ML.js implements the following algorithms. </p> <h3>Classification</h3> <ul> <li> K-nearest neighbors, </li> <li> Linear/quadratic discriminant analysis, </li> <li> Naive Bayes classifier, </li> <li> Logistic regression, </li> <li> Perceptron, </li> <li> Multi-layer perceptron, </li> <li> Support vector machines, </li> <li> Multi-class support vector machines, </li> <li> Decision trees </li> </ul> <h3>Regression</h3> <ul> <li> Least squares, </li> <li> Least absolute devations, </li> <li> K-nearest neighbors, </li> <li> Ridge regression, </li> <li> LASSO, </li> <li> LARS, </li> <li> Orthogonal least squares, </li> <li> Multi-layer perceptron, </li> <li> Kernel ridge regression, </li> <li> Support vector regression, </li> <li> K-LinReg </li> </ul> <h3>Clustering</h3> <ul> <li> K-means, </li> <li> Spectral clustering </li> </ul> <h3>Dimensionality reduction</h3> <ul> <li> Principal component analysis, </li> <li> Locally linear embedding, </li> <li> Local tangent space alignment </li> </ul> <p>Documentation is available at <a href=""></a> </p> <h2>LALOLab: a matlab-like development environment</h2> <p>Try it at <a href=""></a> </p></html>fabien lauer, pedro ernesto garcia rodriguezFri, 23 Feb 2018 15:40:27 -0000 reductionlinear algebradevelopment environmentscientific computingweb