
 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,... ),
 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 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:
 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)
 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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