Support Vector Mind Map of Wine Speak

  • Brendan FlanaganEmail author
  • Sachio Hirokawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9734)


Models created by blackbox machine learning techniques such as SVM can be difficult to interpret. It is because these methods do not offer a clear explanation of how classifications are derived that is easy for humans to understand. Other machine learning techniques, such as: decision trees, produce models that are intuitive for humans to interpret. However, there are often cases where an SVM model will out preform a more intuitive model, making interpretation of SVM trained models an important problem. In this paper, we propose a method of visualizing linear SVM models for text classification by analyzing the relation of features in the support vectors. An example of this method is shown in a case study into the interpretation of a model trained on wine tasting notes.


Model visualization SVM Support vector weight 



This work was supported by JSPS KAKENHI Grant Number 15J04830.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Graduate School of Information Science and Electrical EngineeringKyushu UniversityFukuokaJapan
  2. 2.Research Institute for Information TechnologyKyushu UniversityFukuokaJapan

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