Download JAR files tagged by output with all dependencies

The biblio transformation engine is a java framework developed by the Hellenic National Documentation Centre (EKT, www.ekt.gr) and consists of programmatic APIs for filtering and modifying records that are retrieved from various types of data sources (eg. databases, files, legacy data sources) as well as for outputing them in appropriate standards formats (eg. database files, txt, xml, Excel). The framework includes independent abstract modules that are executed seperately, offering in many cases alternative choices to the user depending of the input data set, the transformation workflow that needs to be executed and the output format that needs to be generated.

This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999. This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance).

The biblio transformation engine is a java framework developed by the Hellenic National Documentation Centre (EKT, www.ekt.gr) and consists of programmatic APIs for filtering and modifying records that are retrieved from various types of data sources (eg. databases, files, legacy data sources) as well as for outputing them in appropriate standards formats (eg. database files, txt, xml, Excel). The framework includes independent abstract modules that are executed seperately, offering in many cases alternative choices to the user depending of the input data set, the transformation workflow that needs to be executed and the output format that needs to be generated.

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.

StringTemplate is a java template engine for generating source code, web pages, emails, or any other formatted text output. StringTemplate is particularly good at multi-targeted code generators, multiple site skins, and internationalization/localization. It evolved over years of effort developing jGuru.com. StringTemplate also generates the stringtemplate website: http://www.stringtemplate.org and powers the ANTLR v3 code generator. Its distinguishing characteristic is that unlike other engines, it strictly enforces model-view separation. Strict separation makes websites and code generators more flexible and maintainable; it also provides an excellent defense against malicious template authors. There are currently about 600 StringTemplate source downloads a month.

A great way to output Xml. Far easier to code with than painful DOM or SAX like solutions and much nicer in terms of speed and memory usage. <br/> <br/> While XmlWriter contains its own xml outputter, it has the ability to sit on top of other core Xml writing products, such as <a href="http://xmlenc.sf.net/">XmlEnc</a>. In addition, the user may layer <a href="Optional.html">other functionalities</a> on top of the core writing, such as on the fly schema checking, date/number formatting, specific empty-element handling and pretty-printing.

Tool used to generate the preferred class information for downloadable JAR files in the form of a META-INF/PREFERRED.LIST required for use by the {@link net.jini.loader.pref.PreferredClassLoader}. The list is generated by examining the dependencies of classes contained within a target JAR file and zero or more additional supporting JAR files. Through various command-line options, a set of "root" classes are identified as belonging to a public API. These root classes provide the starting point for recursively computing a dependency graph, finding all of the classes referenced in the public API of the root classes, finding all of the classes referenced in turn by the public API of those classes, and so on, until no new classes are found. The results of the dependency analysis are combined with the preferred list information in the additional supporting JAR files to compute a preferred list having the smallest number of entries that describes the preferred state of the classes and resources contained in all of the JAR files. The output of the tool is a new version of the target JAR file containing the generated preferred list, and/or a copy of the list printed to System.out.

This module defines the interfaces for Comparators in the TCS Alignment Toolbox. A Comparator has the purpose of defining the dissimilarity between elements in the input sequences of an Alignment. More specific information on Comparators can be found in the 'Comparator' interface. You can find a lot of helpful standard implementations of Comparators in the comparators-lib module. In the TCS Alignment Toolbox we require the output values of Comparators to lie in the range [0,1]. Many natural dissimilarities on value sets do not meet this criterion, such that additional normalization has to be applied. To that end this package also contains a Normalizer interface for functions that map real values from the range [0, infinity) to the range [0,1]. This package also provides a few convenience implementations of the Comparator interface to make the implementation of custom Comparators simpler, namely: SkipExtendedComparator, ParameterLessSkipExtendedComparator, ComparisonBasedSkipExtendedComparator, and ParameterLessComparisonBasedSkipExtendedComparator. Finally the TCS Alignment Toolbox also provides the means to learn parameters of Comparators. To enable that Comparators must implement the DerivableComparator interface to properly define the parameters that can be learned and the gradient of the dissimilarity with respect to these parameters. Gradients are stored using the Gradient interface as well as some convenience implementations of said interface, namely EmptyGradient, SingletonGradient, ArrayGradient and ListGradient.

The libswresample library performs highly optimized audio resampling, rematrixing and sample format conversion operations. Specifically, this library performs the following conversions: Resampling: is the process of changing the audio rate, for example from an high sample rate of 44100Hz to 8000Hz. Audio conversion from high to low sample rate is a lossy process. Several resampling options and algorithms are available. Format conversion: is the process of converting the type of samples, for example from 16-bit signed samples to unsigned 8-bit or float samples. It also handles packing conversion, when passing from packed layout (all samples belonging to distinct channels interleaved in the same buffer), to planar layout (all samples belonging to the same channel stored in a dedicated buffer or "plane"). Rematrixing: is the process of changing the channel layout, for example from stereo to mono. When the input channels cannot be mapped to the output streams, the process is lossy, since it involves different gain factors and mixing. Various other audio conversions (e.g. stretching and padding) are enabled through dedicated options.

Performs one-class classification on a dataset. Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class. Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any classes. For more information, see: Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.