Project details for MLweb

Logo MLweb 0.1.4

by lauerfab - June 28, 2016, 16:00:52 CET [ Project Homepage BibTeX Download ]

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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,... ),
  • statistics: sampling from and estimating standard distributions,
  • optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming.

Documentation is available at

See also the benchmark at

ML.js: a javascript library for machine learning

In addition to all the functions of LALOLib, ML.js implements the following algorithms.


  • K-nearest neighbors,
  • Linear discriminant analysis,
  • Naive Bayes classifier,
  • Logistic regression,
  • Perceptron,
  • Multi-layer perceptron,
  • Support vector machines,
  • Multi-class support vector machines,
  • Decision trees


  • Least squares,
  • Least absolute devations,
  • K-nearest neighbors,
  • Ridge regression,
  • LASSO,
  • LARS,
  • Orthogonal least squares,
  • Multi-layer perceptron,
  • Kernel ridge regression,
  • Support vector regression,
  • K-LinReg


  • K-means,
  • Spectral clustering

Dimensionality reduction

  • Principal component analysis,
  • Locally linear embedding,
  • Local tangent space alignment

Documentation is available at

LALOLab: a matlab-like development environment

Try it at

Changes to previous version:
  • 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
BibTeX Entry: Download
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


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