About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning.

New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance

New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

• Common Core:
  • Added checkedAdd and checkedMultiply functions to MathUtil, providing a means for conducting Integer addition and multiplication with explicit checking for overflow and underflow, and throwing an ArithmeticException if they occur. Java fails silently in integer over(under)flow situations.
  • Added explicit integer overflow checks to DenseMatrix. The underlying MTJ library stores dense matrices as a single dimensional arrays of integers, which in Java are 32-bit. When creating a matrix with numRows rows and numColumns columns, if numRows * numColumns is more than 2^31 - 1, a silent integer overflow would occur, resulting in later ArrayIndexOutOfBoundsExceptions when attempting to access matrix elements that didn't get allocated.
  • Added new methods to DiagonalMatrix interface for multiplying diagonal matrices together and for inverting a DiagonalMatrix.
  • Optimized operations on diagonal matrices in DiagonalMatrixMTJ.
  • Added checks to norm method in AbstractVectorSpace and DefaultInfiniteVector for power set to NaN, throwing an ArithmeticException if encountered.
• Learning Core:
  • Optimized matrix multiplies in LogisticRegression to avoid creating dense matrices unnecessarily and to reduce computation time using improved DiagonalMatrix interfaces.
  • Added regularization and explicit bias estimation to MultivariateLinearRegression.
  • Added ConvexReceiverOperatingCharacteristic, which computes the convex hull of the ROC.
  • Fixed rare corner-case bug in ReceiverOperatingCharacteristic and added optional trapezoidal AUC computation.
  • Cleaned up constant in MultivariateCumulativeDistributionFunction and added publication references.