Crafty Birds: Bird Art & Crafts for Mixed Media Artists

Freedom, grace, happiness, simplicity--all are characteristics linked to the standard chicken, an issue favorite by means of artists and crafters everywhere.

Crafty Birds celebrates these nice feathered pals with an enthralling number of mixed-media projects.

From jewellery to university to soldering and more--and from all of your favourite North gentle authors, together with Kelly Rae Roberts, Josie Cirincione and Stephanie Lee--you'll take pleasure in feathering your nest with:

jewellery tasks, together with necklaces, rings and bracelets.
college initiatives, together with paper, textile and electronic media.
Mixed-media tasks, together with assemblage, soldering and paintings journaling.

And you'll research professional suggestions for operating with resin, twine, plaster, encaustic, Adobe Photoshop, and extra! So unfold your wings and get started--Crafty Birds will depart you hovering!

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Extra resources for Crafty Birds: Bird Art & Crafts for Mixed Media Artists

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This chapter investigates three types of recommendation systems (shown in Figures 9, 10, and 11). Fig. 9 Content-meta-based search Fig. 10 Context-aware search Fig. 11 User-preference-based search 2 Cross-category Recommendation for Multimedia Content 41 1) Content-meta-based Search In content-meta-based search, users can acquire relevant content by querying for keywords or features. The content data are initially indexed based on keywords or features by employing a content profiling process.

1) VSM One of the simplest approaches for similarity calculation is using the VSM. This model measures the distance between vectors. The most practical distance measure is the cosine distance, as shown in Figure 7. For example, user preference (UP) and Fig. 7 Example of Similarity in VSM 38 N. Kamimaeda et al. content profile (CP) are expressed as an n-dimensional feature vector in the VSM. c1 ; c2 ; ; ; cn / contents profile vector 2) NB Classifier NB is a probabilistic approach to classify data or infer a hypothesis.

P. 393-399. 6. D. O’Sullivan, B. Smyth, D. C. Wilson, K. McDonald, and A. Smeaton, “Improving the Quality of the Personalized Electronic Program Guide,” User Modeling and User Adapted Interaction;Vol. 14, No. p. 5-36. 7. S. Gutta, K. Kuparati, K. Lee, J. Martino, D. Schaffer, and J. p. 1121-1122. 8. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. p. 175-186. 1 Personalized Movie Recommendation 25 9. J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Communications of the ACM, Vol.

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