Search results for "rapidminer"

TextualAttributeExtraction for Recommender... (1)

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This workflow takes conent data from VideoLectures.Net Recommender System Challenge and extractes word-vectors for each lecture. Latent semantic analysis with Singular Value Decomposition is done on item-word binary matrix. The last step is the binomializaiton of dataset.

Created: 2012-06-03

Offline update recommendation web service V2 (1)

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  Periodically we have to do a full re-training on whole train set by the offline update recommendation web service.

Created: 2012-06-20 | Last updated: 2012-06-20

Online update recommendation web service V2 (1)

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 After some arbitrary number of recommendations to specific users, system has to update recommendations in item recommendation table. This is accomplished by calling the online update recommendation web service, which updates the recommendation model in RapidAnalytics repository and updates the recommendations for specific users in item recommendation table.

Created: 2012-06-20 | Last updated: 2012-06-20

Simple Recommender System Web Service V2 (1)

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No description

Created: 2012-06-20

Instance Selection and Prototype Based Rul... (1)

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Example of Instance selection (Requires Instance Selection and Prototype Based Rules plugin): Before training kNN we do instance selection based on ENN algorithm. The ENN algorithm can be replaced by any other instance selection algorithms from Prules/Selection/* Instance selection algorithms form PRules plugin works as View on original ExampleSet

Created: 2011-11-05 | Last updated: 2011-11-05

Instance Selection and Prototype Based Rul... (1)

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Example of Instance selection chain (Requires Instance Selection and Prototype Based Rules plugin): Beafore training kNN we do instance selection based on cascade of instance selection algorithms. Here ENN algorithm is followed by CNN algorithm. Instance selection algorithms form PRules plugin works as View on original ExampleSet

Created: 2011-11-05

Instance Selection and Prototype Based Rul... (1)

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Example of Instance selection: (Requires Instance Selection and Prototype Based Rules plugin): Here instance selection is based on All-kNN algorithm. Because the Pro output of any instance selection operator is just an ExampleSet object, so in fact any Learner can be used instead of kNN, like DecisionTree, but in this example the Decision Tree is based on the reduced ExampleSet .

Created: 2011-11-05

Instance Selection and Prototype Based Rul... (2)

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Example of Instance optimization (Requires Instance Selection and Prototype Based Rules): Here we use FCM (Fuzzy C-means) clustering to initialize kNN classifier. The "Class assigner" is responsible for assigning class label for each cluster center. The class assigner operator use Voronoi diagram and the majority voting for determining class label.

Created: 2011-11-05 | Last updated: 2011-11-07

Instance Selection and Prototype Based Rul... (1)

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Example of Instance optimization - using clustering for training kNN classifier (Requires Instance Selection and Prototype Based Rules) : Here we use FCM (Fuzzy c-means) clustering to initialize kNN classifier. Moreover centers of clusters are determined independent for each class. "Class iterator" operator iterates over each class label, and embedded clustering algorithm cluster the examples for each class independent. The Prototype (Pro) output of "Class iterator" delivers the concatena...

Created: 2011-11-05 | Last updated: 2011-11-05

Instance Selection and Prototype Based Rul... (2)

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Example of Instance optimization using LVQ algorithm for training kNN classifier (Requires Instance Selection and Prototype Based Rules): Here we use FCM clustering to initialize LVQ network. The "Class assigner" is responsible for assigning class label for each cluster center, then obtained ExampleSet is used as codebooks initialization in the LVQ operator, which on the Prototype (Pro) output delivers the new optimized position of codebooks (prototypes) for training nearest n...

Created: 2011-11-05 | Last updated: 2011-11-07