By Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)
The two-volume set LNCS 6593 and 6594 constitutes the refereed court cases of the tenth foreign convention on Adaptive and common Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers provided have been rigorously reviewed and chosen from a complete of one hundred forty four submissions. the second one quantity contains forty-one papers equipped in topical sections on trend reputation and studying, delicate computing, platforms idea, help vector machines, and bioinformatics.
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Extra resources for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II
Here, four clusters are deﬁned, containing 104, 150, 150 and 110 2-D points, from the topmost to the lowermost cluster respectively. f) Data set Flag consists of 640 points that form three clusters. The spherical cluster in the middle contains 100 2-D points; the cluster above and the cluster beneath contain 270 points each. g) Iris data set  has been widely used in classiﬁcation tasks. It has 150 points of four dimensions, divided into three classes of an iris plant with 50 points each. The ﬁrst class is linearly separable from the other two.
These are used to reveal the overall importance of individual features and their contribution to the model’s predictions. When interested in a speciﬁc prediction, we observe the corresponding instance explanation. For example, Figure 11 is an instance explanation for MP’s prediction for a particular concrete mixture. MP’s prediction for is close to the actual concrete compressive strength, while LR overestimates the compressive strength for this instance and predicts 60 MPa. The explanation reveals which features contribute towards/against compressive strength.
337–348. Springer, Heidelberg (2004) 6. : Svm and graphical algorithms: A cooperative approach. In: 4th IEEE ICDM, pp. 499–502 (2004) ˇ 7. : Explaining classiﬁcations for individual instances. IEEE TKDE 20, 589–600 (2008) ˇ 8. : An eﬃcient explanation of individual classiﬁcations using game theory. Journal of Machine Learning Research 11, 1–18 (2010) 9. : The feature importance ranking measure. , Shawe-Taylor, J. ) ECML PKDD 2009. LNCS, vol. 5782, pp. 694–709. Springer, Heidelberg (2009) An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring (NILM) Systems Marisa B.