NDLI: Interpreting individual classifications of hierarchical networks

Content Provider	IEEE Xplore Digital Library
Author	Landecker, W. Thomure, M.D. Bettencourt, L.M.A. Mitchell, M. Kenyon, G.T. Brumby, S.P.
Copyright Year	2013
Description	Author affiliation: Los Alamos Nat. Lab., Los Alamos, NM, USA (Kenyon, G.T.; Brumby, S.P.) \|\| Dept. of Comput. Sci., Portland State Univ., Portland, OR, USA (Landecker, W.; Thomure, M.D.; Mitchell, M.) \|\| Santa Fe Inst., Santa Fe, NM, USA (Bettencourt, L.M.A.)
Abstract	Hierarchical networks are known to achieve high classification accuracy on difficult machine-learning tasks. For many applications, a clear explanation of why the data was classified a certain way is just as important as the classification itself. However, the complexity of hierarchical networks makes them ill-suited for existing explanation methods. We propose a new method, contribution propagation, that gives per-instance explanations of a trained network's classifications. We give theoretical foundations for the proposed method, and evaluate its correctness empirically. Finally, we use the resulting explanations to reveal unexpected behavior of networks that achieve high accuracy on visual object-recognition tasks using well-known data sets.
Starting Page	32
Ending Page	38
File Size	946681
Page Count	7
File Format	PDF
ISBN	9781467358958
DOI	10.1109/CIDM.2013.6597214
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy Shape Vectors Mathematical model Kernel Equations
Content Type	Text
Resource Type	Article

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