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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Interpretability of Computational Intelligence-Based Regression Models

Interpretability of Computational Intelligence-Based Regression Models

Abonyi, János
Kenesei, Tamás

Paperback

Series: Springerbriefs in Computer Science

Technology & EngineeringDatabasesGeneral Computers

ISBN10: 3319219413
ISBN13: 9783319219417
Publisher: Springer Nature
Published: Nov 10 2015
Pages: 82
Weight: 0.32
Height: 0.19 Width: 6.14 Depth: 9.21
Language: English

The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression.

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