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

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

Regression

• 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

Clustering

• K-means,
• 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 matlab-like development environment

Try it at http://mlweb.loria.fr/lalolab/

Changes to previous version:

• 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

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

Comments

No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.