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Mean Absolute Percentage Error for regression models
TLDR
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. Expand
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The State of the Art in Integrating Machine Learning into Visual Analytics
TLDR
Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning to make sense of large, complex data. Expand
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Mutual information for the selection of relevant variables in spectrometric nonlinear modelling
TLDR
The mutual information measure measures the information content in input variables with respect to the model output, without making any assumption on the model that will be used. Expand
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Exploratory analysis of functional data via clustering and optimal segmentation
TLDR
We propose in this paper an exploratory analysis algorithm for functional data. Expand
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Resampling methods for parameter-free and robust feature selection with mutual information
TLDR
Combining the mutual information criterion with a forward feature selection strategy offers a good trade-off between optimality of the selected feature subset and computation time. Expand
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A bag-of-paths framework for network data analysis
TLDR
This work develops a generic framework, called the bag-of-paths (BoP), for link and network data analysis. Expand
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Discovering patterns in time-varying graphs: a triclustering approach
TLDR
This paper introduces a novel technique to track structures in time varying graphs. Expand
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Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101)
TLDR
This report documents the program and the outcomes of Dagstuhl Seminar 15101 "Bridging Information Visualization with Machine Learning". Expand
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A Triclustering Approach for Time Evolving Graphs
TLDR
This paper introduces a novel technique to track structures in time evolving graphs. Expand
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Accelerating Relational Clustering Algorithms With Sparse Prototype Representation
TLDR
In some application contexts, data are better described by a matrix of pairwise dissimilarities rather than by a vector representation. Expand
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