About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate classifiers without writing a single line of code.Changes:
Many fixes and new functionalities are included in this version. Among them, an efficient and full version of the Smoothed Partial Tree Kernel is for the first time available to the public.
Check out this new version from our repositories. Soon we will upload new versions of the documentation pages, while API Javadoc is already available.
Your suggestions will be very precious for us, so download and try KeLP 1.1.0!
New Representations: - SequenceRepresentation
New Kernels: - SubSetTreeKernel - SmoothedPartialTreeKernel - CompositionallySmoothedPartialTreeKernel - SequenceKernel
New LearningAlgorithms: - LibLinearRegression - BudgetedPassiveAggressive
About: Multicore/distributed large scale machine learning framework.Changes: