Download JAR files tagged by evaluation with all dependencies

MELT is a helpful maven framework for developing, tuning, evaluating, and packaging ontology matching systems. It is optimized to be used in OAEI campaigns and allows to submit matchers to the SEALS and HOBBIT evaluation platform easily. MELT can also be used for non OAEI-related matching tasks and evaluation.

The JINSECT toolkit is a Java-based toolkit and library that supports and demonstrates the use of n-gram graphs within Natural Language Processing applications, ranging from summarization and summary evaluation to text classification and indexing.

Implements Waiting for JUnit tests. Concept contains expressions which are responsible to retrieve a state of the application, matcher to verify the expected state (from Hamcrest Library) and conditions which are responsible to repeat the evaluation of the given expression until the state matches the expected state.

This package provides two classes - one for evaluating the merit of individual attributes using a classifier (ClassifierAttributeEval), and second for evaluating the merit of subsets of attributes using a classifier (ClassifierSubsetEval). Both invoke a user-specified classifier to perform the evaluation, either under cross-validation or on the training data.

Implements Grading. The base classifiers are "graded". For more information, see A.K. Seewald, J. Fuernkranz: An Evaluation of Grading Classifiers. In: Advances in Intelligent Data Analysis: 4th International Conference, Berlin/Heidelberg/New York/Tokyo, 115-124, 2001.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to stream data to MOA from Kafka.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to use MOA from within WEKA.

AuthzForce PDP extensions for JSON value processing, in particular JSONPath evaluation; it provides the XACML datatype 'urn:ow2:authzforce:feature:pdp:datatype:json' for JSON object/array values, and XACML functions that evaluate a JSON path (second parameter of standard string datatype) against an input JSON object/array (first parameter of datatype 'urn:ow2:authzforce:feature:pdp:datatype:json') and return the result of this evaluation. The return datatype depends on the actual function used: 'urn:ow2:authzforce:feature:pdp:function:string-from-json-path' returns a bag of strings, 'urn:ow2:authzforce:feature:pdp:function:integer-from-json-path' returns a bag of integers, 'urn:ow2:authzforce:feature:pdp:function:double-from-json-path' returns a bag of doubles, 'urn:ow2:authzforce:feature:pdp:function:boolean-from-json-path' returns a bag of booleans.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to stream data to MOA from Kafka.

Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems. This artifact enables you to use MOA from within WEKA.