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org.apache.stanbol.enhancer.engine.disambiguation.mlt from group org.apache.stanbol (version 1.0.0)
Entity Disambiguation Enhancement Engine that performs Similarity queries on
the Stanbol Entityhub to re-rank suggested Entities.
Group: org.apache.stanbol Artifact: org.apache.stanbol.enhancer.engine.disambiguation.mlt
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Artifact org.apache.stanbol.enhancer.engine.disambiguation.mlt
Group org.apache.stanbol
Version 1.0.0
Last update 16. September 2016
Organization not specified
URL Not specified
License not specified
Dependencies amount 6
Dependencies org.apache.stanbol.commons.stanboltools.offline, org.apache.stanbol.enhancer.servicesapi, org.apache.stanbol.enhancer.nlp, org.apache.stanbol.entityhub.servicesapi, org.apache.felix.scr.annotations, commons-lang,
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Group org.apache.stanbol
Version 1.0.0
Last update 16. September 2016
Organization not specified
URL Not specified
License not specified
Dependencies amount 6
Dependencies org.apache.stanbol.commons.stanboltools.offline, org.apache.stanbol.enhancer.servicesapi, org.apache.stanbol.enhancer.nlp, org.apache.stanbol.entityhub.servicesapi, org.apache.felix.scr.annotations, commons-lang,
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latentSemanticAnalysis from group nz.ac.waikato.cms.weka (version 1.0.5)
Performs latent semantic analysis and transformation of the data. Use in conjunction with a Ranker search. A low-rank approximation of the full data is found by specifying the number of singular values to use. The dataset may be transformed to give the relation of either the attributes or the instances (default) to the concept space created by the transformation.
Group: nz.ac.waikato.cms.weka Artifact: latentSemanticAnalysis
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Artifact latentSemanticAnalysis
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 21. March 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/latentSemanticAnalysis
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 21. March 2017
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/latentSemanticAnalysis
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
rng from group de.cit-ec.ml (version 1.0.0)
This is an implementation of the Neural Gas algorithm on
distance data (Relational Neural Gas) for unsupervised clustering.
We recommend that you use the functions provided by the RelationalNeuralGas
class for your purposes. All other classes and functions are utilities which
are used by this central class. In particular, you can use RelationalNeuralGas.train()
to obtain a RNGModel (i.e. a clustering of your data), and subsequently
you can use RelationalNeuralGas.getAssignments() to obtain the resulting
cluster assignments, and RelationalNeuralGas.classify() to cluster new points
which are not part of the training data set.
The underlying scientific work is summarized nicely in the dissertation
"Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss
(2009).
The basic properties of an Relational Neural Gas algorithm are the following:
1.) It is relational: The data is represented only in terms of a pairwise
distance matrix.
2.) It is a clustering method: The algorithm provides a clustering model,
that is: After calculation,
each data point should be assigned to a cluster (for this package here we
only consider hard clustering, that is: each data point is assigned to
exactly one cluster).
3.) It is a vector quantization method: Each cluster corresponds to a
prototype, which is in the center of the
cluster and data points are assigned to the cluster if and only if they are
closest to this particular prototype.
4.) It is rank-based: The updates of the prototypes depend only on
the distance ranking, not on the absolute value of the distances.
Artifact rng
Group de.cit-ec.ml
Version 1.0.0
Last update 26. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/relational_neural_gas
License The GNU General Public License, Version 3
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group de.cit-ec.ml
Version 1.0.0
Last update 26. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/relational_neural_gas
License The GNU General Public License, Version 3
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
raceSearch from group nz.ac.waikato.cms.weka (version 1.0.2)
Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes:
forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set.
Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions.
Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set.
Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc.
It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes.
Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.
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Artifact raceSearch
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/raceSearch
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, classifierBasedAttributeSelection,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/raceSearch
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, classifierBasedAttributeSelection,
There are maybe transitive dependencies!
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