Projects supporting the json data format.


Logo KeLP 1.2.1

by kelpadmin - July 24, 2015, 15:43:13 CET [ Project Homepage BibTeX Download ] 2850 views, 725 downloads, 3 subscriptions

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:

The code for learning relations between pairs of short texts has been released, and includes the approach described in:

Simone Filice, Giovanni Da San Martino and Alessandro Moschitti. Relational Information for Learning from Structured Text Pairs. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015.

In particular this new release includes:

  • TreePairRelTagger: a manipulator that establishes relations between two tree representations (available in the maven project discreterepresentation)

  • 5 new kernels on pairs: released in the maven project standard-kernel

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 1.2.1!


Logo MOSIS 0.55

by claasahl - March 9, 2014, 17:35:40 CET [ BibTeX Download ] 4175 views, 1365 downloads, 2 subscriptions

About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications.

Changes:
  • Move "flow"-related classes into package "de.claas.mosis.flow" (e.g. Node and Link).
  • Refined and improved "flow"-related tests (e.g. Iterator and Node tests).
  • Refactored tests for data formats (e.g. PlainText and JSON tests).
  • Added visitor design pattern for graph-based functions (e.g. initialization and processing).
  • Documented parameters of Processor implementations.

Logo OptWok 0.3.1

by ong - May 2, 2013, 10:46:11 CET [ Project Homepage BibTeX Download ] 8898 views, 1731 downloads, 1 subscription

About: A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems. Used in: - Ellipsoidal multiple instance learning - difference of convex functions algorithms for sparse classfication - Contextual bandits upper confidence bound algorithm (using GP) - learning output kernels, that is kernels between the labels of a classifier.

Changes:
  • minor bugfix