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multiLayerPerceptrons from group nz.ac.waikato.cms.weka (version 1.0.10)
This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.
Group: nz.ac.waikato.cms.weka Artifact: multiLayerPerceptrons
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Artifact multiLayerPerceptrons
Group nz.ac.waikato.cms.weka
Version 1.0.10
Last update 31. October 2016
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/multiLayerPerceptrons
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.10
Last update 31. October 2016
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/multiLayerPerceptrons
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
mydas from group uk.ac.ebi.mydas (version 1.0.2)
This project aims to offer an easy-to-extend Java DAS server framework.
It offers several advantages:
* Implementing data sources is very easy but also flexible and powerful.
* Data caching is built into the system, with access to the caching
mechanism made available to the data sources.
* All aspects of the server are highly configurable, including
selecting options where the DAS 1.53 specification offers
choices to the implementor.
* The latest Java technologies have been used throughout the
system to optimise performance and simplify data source development.
* Wherever possible the same terminology is used in the API as in
the DAS specification and XML - again, making data source
development more easy.
* The server allows XSLT transforms of the DAS XML to be configured
to provide a simple DAS client view (limited to the single DAS
source).
More details of the DAS protocol, DAS servers and DAS clients can
be found at http://www.biodas.org/wiki/Main_Page.
The first version of this server is a complete implementation of
Distributed Sequence Annotation System (DAS) Version 1.53.
If you are interested in learning more about DAS 1.53, the
specification is highly recommended as a concise and complete
description of the DAS protocol that can be obtained from:
http://biodas.org/documents/spec.html
Artifact mydas
Group uk.ac.ebi.mydas
Version 1.0.2
Last update 19. August 2007
Organization not specified
URL http://code.google.com/p/mydas/
License The Apache Software License 2.0
Dependencies amount 7
Dependencies commons-collections, servlet-api, log4j, xpp3, xercesImpl, oscache, commons-logging,
There are maybe transitive dependencies!
Group uk.ac.ebi.mydas
Version 1.0.2
Last update 19. August 2007
Organization not specified
URL http://code.google.com/p/mydas/
License The Apache Software License 2.0
Dependencies amount 7
Dependencies commons-collections, servlet-api, log4j, xpp3, xercesImpl, oscache, commons-logging,
There are maybe transitive dependencies!
mahout from group org.apache.mahout (version 14.1)
Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to
reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative
filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict
contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop
cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for
non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially
friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive,
diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come
to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining
takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents
and groups them into groups of topically related documents. Classification learns from existing categorized
documents what documents of a specific category look like and is able to assign unlabelled documents to the
(hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session,
shopping cart content) and identifies, which individual items usually appear together.
Group: org.apache.mahout Artifact: mahout
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Artifact mahout
Group org.apache.mahout
Version 14.1
Last update 16. July 2020
Organization The Apache Software Foundation
URL http://mahout.apache.org
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.apache.mahout
Version 14.1
Last update 16. July 2020
Organization The Apache Software Foundation
URL http://mahout.apache.org
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
xapi-gwt-parent from group net.wetheinter (version 0.5)
This is the main aggregator for all gwt submodules.
All gwt-specific code resides here.
Submodules should avoid inheriting from each other unless necessary.
This goes for maven structure and gwt.xml structure.
The super module is where our jre emulation layer and super-source live;
all modules should inherit super, and a minimum of other modules.
Some modules, like injection, are fulfilling an api in the core module,
and should be accessed only through core service interfaces.
Other modules, like reflection, are capable of being standalone inherits,
but can benefit from core utilities like injection,
so, two (or more) .gwt.xml modules may be provided.
As XApi nears 1.0, all submodules will be routinely stitched together into
an uber-jar, in order to have a single jar with a single gwt module
that can provide all of the services at once.
Internal projects will never use the uber jar, to help maintain modularity,
but external projects that want to use more than one service
will certainly prefer inheriting one artifact, instead of twelve.
When distributed in uber-jar format, it will likely be necessary for
either the uber jar, or just xapi-gwt-api.jar to appear before gwt-dev
on your compile-time classpath. If using gwt-maven-plugin, the
gwtFirstOnClasspath option may become problematic. If so, we will provide
a forked gwt-plugin to make sure our compiler enhancements are included in
the build process.
There is also work going on to make a super-source-everything plugin,
which will use maven to find source files, and generate synthetic .gwt.xml
for you, as part of an effort to create a wholly unified programming
environment. In addition to java-to-javascript, we intend to compile
java-to-java and possibly other languages, like go; imagine implementing
gwt deferred binding to eliminate cross-platform differences between
server environments, or operating systems, or versions of a platform,
or anywhere else a core api needs to bind to multiple implementations,
depending on the runtime environment.
Group: net.wetheinter Artifact: xapi-gwt-parent
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Artifact xapi-gwt-parent
Group net.wetheinter
Version 0.5
Last update 30. May 2015
Organization not specified
URL WeTheInter.net
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group net.wetheinter
Version 0.5
Last update 30. May 2015
Organization not specified
URL WeTheInter.net
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
mahout-eclipse-support from group org.apache.mahout (version 0.5)
Artifact mahout-eclipse-support
Group org.apache.mahout
Version 0.5
Last update 28. May 2011
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.apache.mahout
Version 0.5
Last update 28. May 2011
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
mahout-parent from group org.apache.mahout (version 0.3)
Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.
Group: org.apache.mahout Artifact: mahout-parent
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Artifact mahout-parent
Group org.apache.mahout
Version 0.3
Last update 12. March 2010
Organization The Apache Software Foundation
URL http://lucene.apache.org/mahout
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.apache.mahout
Version 0.3
Last update 12. March 2010
Organization The Apache Software Foundation
URL http://lucene.apache.org/mahout
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
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