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 prediction models without writing a single line of code.
Changes:
In addition to minor improvements and bug fixes, this release includes:
The possibility to generate the Compositional GRCT and the Compositional LCT data structures in kelp-input-generator.
New metrics for evaluating Classification Tasks.
New Tutorial and Unit Tests.
Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.2.2!
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- Operating System:
Platform Independent
- Data Formats:
Csv,
Libsvm,
Json,
Multiple Representations Format
- Tags:
Svm,
Classification,
Clustering,
Regression,
Kernels,
Online Learning,
Kernel Methods,
Graph Kernels,
Structured Data,
Linear Models,
Tree Kernels
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