From rifting to drifting

From Lithospheric Thickening and Divergent Collapse to Active Continental Rifting

Symmetric alternative to asymmetric rifting models

Figure 1. Schematic cross sections through (A) Galicia Margin west of Iberian Peninsula (Boillot et al., 1995) and (B) Jurassic margin of Apulian continent toward Alpine Tethys (Manatschal and Bernoulli, 1999). Inset in B displays present location of margin in Swiss Alps and paleoposition of section in Jurassic time. Abbreviations in A: lcc—lower continental crust; man— mantle; S—S-reflector; s...

Adaptation to Drifting Concepts

Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an extended period of time, the learning task can be complicated by changes in the distribution underlying the data. This problem is known in machine learning as concept drift. The main idea behind Statistical Quality Cont...

Learning to Rank from Concept-Drifting Network Data Streams

Networked data are, nowadays, collected in various application domains such as social networks, biological networks, sensor networks, spatial networks, peer-to-peer networks etc. Recently, the application of data stream mining to networked data, in order to study their evolution over time, is receiving increasing attention in the research community. Following this main stream of research, we pr...

Adaptation to Drifting User's Interests

In recent years, many systems have been developed which aim at helping users to find pieces of information or other objects that are in accordance with their personal interests. In these systems, machine learning methods are often used to acquire the user interest profile. Frequently user interests drift with time. The ability to adapt fast to the current user's interests is an important featur...