
 Description:
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.
It includes the following.
LALOLib: a javascript library to enable and ease scientific computing within web pages
LALOLib provides functions for
 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... ),
 statistics: sampling from and estimating standard distributions,
 optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming.
Documentation is available at http://mlweb.loria.fr/lalolab/lalolib.html
See also the benchmark at http://mlweb.loria.fr/benchmark/
ML.js: a javascript library for machine learning
In addition to all the functions of LALOLib, ML.js implements the following algorithms.
Classification
 Knearest neighbors,
 Linear/quadratic discriminant analysis,
 Naive Bayes classifier,
 Logistic regression,
 Perceptron,
 Multilayer perceptron,
 Support vector machines,
 Multiclass support vector machines,
 Decision trees
Regression
 Least squares,
 Least absolute devations,
 Knearest neighbors,
 Ridge regression,
 LASSO,
 LARS,
 Orthogonal least squares,
 Multilayer perceptron,
 Kernel ridge regression,
 Support vector regression,
 KLinReg
Clustering
 Kmeans,
 Spectral clustering
Dimensionality reduction
 Principal component analysis,
 Locally linear embedding,
 Local tangent space alignment
Documentation is available at http://mlweb.loria.fr/lalolab/lalolib.html
LALOLab: a matlablike development environment
Try it at http://mlweb.loria.fr/lalolab/
 Changes to previous version:
 Add support for complex numbers, vectors and matrices
 Add basic signal processing (discrete Fourier transform, sound())
 Add quadratic discriminant analysis
 Faster Cholesky factorization
 BibTeX Entry: Download
 URL: Project Homepage
 Supported Operating Systems: Linux, Windows, Platform Independent, Mac Os X
 Data Formats: Ascii, Csv, Libsvm, Json
 Tags: Classification, Clustering, Regression, Dimensionality Reduction, Linear Algebra, Development Environment, Scientific Computing, Web
 Archive: download here
Other available revisons

Version Changelog Date 1.0  Faster LeastSquares and RidgeRegression with conjugate gradient method
 LeastSquares now works also with sparse X
 Faster thin SVD for tall matrices
 Fix load data file in LALOLab
 Add examples in LALOLab
July 7, 2017, 14:43:52 0.1.6  Add support for complex numbers, vectors and matrices
 Add basic signal processing (discrete Fourier transform, sound())
 Add quadratic discriminant analysis
 Faster Cholesky factorization
June 1, 2017, 11:48:19 0.1.5  Optimize use of kernel cache in MSVM.tune()
 A few other speedups (for spectral clustering, eigs, ...)
 Add colormap() to Lalolab for colormap plots
 Changes in some examples
 Minor bug fixes (including plots in IE)
January 17, 2017, 15:47:41 0.1.4  Add Logistic Regression
 Add support for sparse input in fast training of linear SVM
 Better support for sparse vectors/matrices
 Fix plot windows in IE
 Minor bug fixes
June 28, 2016, 16:00:52 0.1.3  Improve NaiveBayes classifier
 Add online training functions for KNN and NaiveBayes
 Fix save/load workspace in LALOLab
 Fix nullspace()
 Small bug fixes
December 17, 2015, 10:29:35 0.1.2  Add Regression:AutoReg method
 Add KernelRidgeRegression tuning function
 More efficient predictions for KRR, SVM, SVR
 Add BFGS optimization method
 Faster QR, SVD and eigendecomposition
 Better support for sparse vectors and matrices
 Add linear algebra benchmark at http://mlweb.loria.fr/benchmark/
 Fix plots in LALOlib/ML.js
 Fix crossorigin issues in new MLlab()
 Small bug fixes
October 9, 2015, 11:55:52 0.1.1  Smaller source package
 Fix Makefile
 Fix MathJax path
September 22, 2015, 09:57:44 0.1  Changed name of MLlib for ML.js
 Improved documentation for ML.js
September 15, 2015, 14:06:39
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