Download JAR files tagged by either with all dependencies

An instance filter that collapses instances with a common grouping ID value into a single instance. Useful for converting transactional data into a format that Weka's association rule learners can handle. IMPORTANT: assumes that the incoming batch of instances has been sorted on the grouping attribute. The values of nominal attributes are converted to indicator attributes. These can be either binary (with f and t values) or unary with missing values used to indicate absence. The later is Weka's old market basket format, which is useful for Apriori. Numeric attributes can be aggregated within groups by computing the average, sum, minimum or maximum.

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. Can either minimize the hinge loss (SVM) or log loss (logistic regression). For more information, see S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.

Extension of BestFirst. Takes a restricted number of k attributes into account. Fixed-set selects a fixed number k of attributes, whereas k is increased in each step when fixed-width is selected. The search uses either the initial ordering to select the top k attributes, or performs a ranking (with the same evalutator the search uses later on). The search direction can be forward, or floating forward selection (with opitional backward search steps). For more information see: Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany.

TagSoup is a SAX-compliant parser written in Java that, instead of parsing well-formed or valid XML, parses HTML as it is found in the wild: poor, nasty and brutish, though quite often far from short. TagSoup is designed for people who have to process this stuff using some semblance of a rational application design. By providing a SAX interface, it allows standard XML tools to be applied to even the worst HTML. TagSoup also includes a command-line processor that reads HTML files and can generate either clean HTML or well-formed XML that is a close approximation to XHTML.

All Roads Lead to ROME. ROME is a set of Atom/RSS Java utilities that make it easy to work in Java with most syndication formats. Today it accepts all flavors of RSS (0.90, 0.91, 0.92, 0.93, 0.94, 1.0 and 2.0), Atom 0.3 and Atom 1.0 feeds. Rome includes a set of parsers and generators for the various flavors of feeds, as well as converters to convert from one format to another. The parsers can give you back Java objects that are either specific for the format you want to work with, or a generic normalized SyndFeed object that lets you work on with the data without bothering about the underlying format.

All Roads Lead to ROME. ROME is a set of Atom/RSS Java utilities that make it easy to work in Java with most syndication formats. Today it accepts all flavors of RSS (0.90, 0.91, 0.92, 0.93, 0.94, 1.0 and 2.0), Atom 0.3 and Atom 1.0 feeds. Rome includes a set of parsers and generators for the various flavors of feeds, as well as converters to convert from one format to another. The parsers can give you back Java objects that are either specific for the format you want to work with, or a generic normalized SyndFeed object that lets you work on with the data without bothering about the underlying format.

The SAP Java Connector (SAP JCo) is a toolkit that allows a Java application to communicate with any SAP system. It combines an easy to use API with unprecedented flexibility and performance. The package supports both, Java to SAP System as well as SAP System to Java calls. - All SAP Connectors are licensed without additional license fees as part of the respective solution or component license. However, please note that each connector may be used only for connecting external (non-SAP) applications to SAP Systems / SAP Solutions. Scenarios, in which two external (non-SAP) applications are integrated via an SAP Connector, are not allowed. - The redistribution of any connector is not allowed. - All SAP users accessing application functionality through the relevant connector are required to be licensed under a respective solution or component license. To use the SAP JCo with the jco-jar project, you need to either install the SAP JCo jar downloaded from SAP to your local Maven repository (variant a) or deploy it to e.g. an enterprise Maven repository like Nexus or Artifactory (variant b): (a) mvn install:install-file -DgroupId=com.github.easonjim -DartifactId=com.sap.conn.jco.sapjco3 -Dversion=3.0.11 -Dpackaging=jar -Dfile=path/to/sapjco3.jar (b) mvn deploy:deploy-file -DrepositoryId=[your.repo.id] -DgroupId=com.github.easonjim -DartifactId=com.sap.conn.jco.sapjco3 -Dversion=3.0.11 -Dpackaging=jar -Dfile=path/to/sapjco3.jar