Download JAR files tagged by summarized with all dependencies
coherence-jvisualvm from group com.oracle.coherence.incubator (version 13.0.1)
Group: com.oracle.coherence.incubator Artifact: coherence-jvisualvm
Show all versions Show documentation Show source
Show all versions Show documentation Show source
0 downloads
refcodes-licensing from group org.refcodes (version 3.3.9)
The refcodes-licensing artifact is a meta-artifact included as a
dependency into all artifacts which apply the herein contained licensing
terms (usually artifacts of the group org.refcodes). Them refcodes-
licensing terms and conditions can be summarized as below. Please see
the refcodes-licensing artifact of the version being applied to the
artifact in question for the terms and conditions effectively being
applied.
0 downloads
Artifact refcodes-licensing
Group org.refcodes
Version 3.3.9
Last update 07. December 2024
Organization not specified
URL https://bitbucket.org/refcodes/${project.artifactId}
License GNU General Public License (GPL), Version 3.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.refcodes
Version 3.3.9
Last update 07. December 2024
Organization not specified
URL https://bitbucket.org/refcodes/${project.artifactId}
License GNU General Public License (GPL), Version 3.0
Dependencies amount 0
Dependencies No dependencies
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!
Page 1 from 1 (items total 3)
© 2015 - 2024 Weber Informatics LLC | Privacy Policy