Weitere Kapitel dieses Buchs durch Wischen aufrufen
Much of the real-world data have complex dependencies between the individual tuples. For example, the chance that a patient has a particular disease depends on the prevalence of the disease in the immediate neighborhood. One approach to handling such linked data is “collective learning.” In collective learning, one deals with a set of data points taken at a time. The dependencies between the data points are modeled as a graph, with the nodes representing the tuples and the edges between them representing the influence of the tuples on one another. A variety of domains lend themselves naturally to such graph-based modeling. There have been a variety of collective learning and inferencing approaches that have been proposed in the literature. In this talk, I will give a brief introduction to collective learning and describe two applications.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
- Some Applications of Collective Learning
- Springer India
Neuer Inhalt/© ITandMEDIA, Product Lifecycle Management/© Eisenhans | vege | Fotolia