Revisiting (ε, γ, τ)-similarity learning for domain adaptation

The weighted h-categorizer semantics (Hbs) takes as in- put a graph G = hA, w, Ri and assigns for each argument an overall strength, which depends on the basic weight of the

We cannot compute the normalized information distance, which is expressed in terms of the noncomputable Kolmogorov complexities of the objects concerned. Instead, we look at whether

Ferraro and Godin [15] have recently introduced an edit distance between un- ordered quotiented trees based on a comparison of support graph and edit operations that

We obtain some numerical evidence for the extent of the essential spectrum on domains with axisymmetric conical points by computing the roots of explicitly given holomorphic

5 Modifying the Projection Space for Domain Adaptation In this section, we present our two approaches for modifying the similarity based projection space in order to facilitate

Finally, the multi-class case and the semi-supervised domain adaptation (i.e when some labels are available on the target) are cru- cial extensions to add as they would allow us to

Whatever the drying kinetics, same morphological behavior Specific signatures of WP and NPC proteins on the particle formation Different kinds of drying behavior. Whey proteins

The 31 studies selected for systematic mapping were classi- fied based on the six different topics (i.e., Business Ecosystem, Collaboration Systems, Digital Business Ecosystem,