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GroupDocs.Annotation lets you add notes to PDF and Word documents, as well as to image files – all directly from a web browser. It is a convenient web-based tool that doesn’t require any software installation and allows you and your colleagues to annotate documents online. Moreover, with GroupDocs.Annotation, you can add your notes to a document and then send it for approval or review, or share the document with others for online collaborative review in real-time. This way you get feedback faster and can keep everyone’s notes and comments in a single file.

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

HermiT is reasoner for ontologies written using the Web Ontology Language (OWL). Given an OWL file, HermiT can determine whether or not the ontology is consistent, identify subsumption relationships between classes, and much more. This is the maven build of HermiT and is designed for people who wish to use HermiT from within the OWL API. It is now versioned in the main HermiT version repository, although not officially supported by the HermiT developers. The version number of this package is a composite of the HermiT version and an value representing releases of this packaged version. So, 1.3.7.1 is the first release of the mavenized version of HermiT based on the 1.3.7 release of HermiT. This package includes the Jautomata library (http://jautomata.sourceforge.net/), and builds with it directly. This library appears to be no longer under active development, and so a "fork" seems appropriate. No development is intended or anticipated on this code base.

Reco4j is an open source project aims at developing a recommendation framework based on graph data sources. We choose graph databases for several reasons. They are NoSQL databases, so "schemaless". This means that it is possible to extend the basic data structure with intermediate information, i.e. similarity value between item and so on. Moreover, since every information is expressed with properties, nodes and relations, the recommendation process can be customized to work on every graph. Reco4j can be used on every graph where "user" and "item" is represented by node and the preferences are modelled as relationship between them. Current implementation leverage on Neo4j as first graph database integrated in our framework.

A filter that applies the LOF (Local Outlier Factor) algorithm to compute an outlier score for each instance in the data. Can use multiple cores/cpus to speed up the LOF computation for large datasets. Nearest neighbor search methods and distance functions are pluggable. For more information, see: Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.