Description
This workshop will try to borrow and adapt diverse theoretical innovations on probabilistic models and related machine learning methods from other areas and will focus on probabilisticbased data mining methods, including graphbased data mining, topological data mining and other informationtheoreticbased approaches (e.g., entropybased data mining), as well as on the “humanintheloop” concept, supported by an interactive learning and optimization component and in visual analysis of heterogeneous and dynamic data sets. For example in networkbased approaches, statistical extensions of graph theoretical approaches, visualizing networks, epistemological meaning of inferred networks, structural analysis of networks, comparative analysis of networks and networkbased biomarkers are challenges, to mention only a few. Classical mathematical techniques do often not fit well the task of analyzing, comparing, classifying, retrieving complex data sets. Topology (and in particular algebraic topology) is, by its very nature, the part of mathematics which formalizes qualitative aspects of objects; therefore topological data processing and topological data mining well integrates with more classical mathematical tools. For example, persistent homology combines geometry and algebraic topology in the study of pairs (X,f) where X is an object (topological space) and f is a continuous function defined on X (typically with real values). One application is the extraction of topological features of an object out of a cloud of sample points. Features are key to learning and understanding. Another class of applications uses f as a formalization of a classification criterion; in this case various functions can give different criteria, cooperating in a complex classifier. Several problems arise from such settings: One, in the application context, is the choice of suitable functions f. This is generally done heuristically, but it would be necessary to have parametrized spaces of such functions and eventually a selfdriving, optimized choice of f for statistical learning. Another challenge is the construction of good distances. The ones presently available need exponential computation. A third problem concerns functions with multidimensional range: functions from X to R give rise to diagrams whose information is condensed in a discrete (mostly finite) set of points in the plane; but if the range is R^k, the same information is carried by (2k2) dimensional patches in R^2k. A onedimensional reduction is available, but it raises computational problems in applications.Period  24 Jul 2015 → 26 Jul 2015 

Event type  Conference 
Conference number  BIRS15w2181 
Location  Banff, CanadaShow on map 
Keywords
 Machine Learning
 Health Informatics
 HCIKDD
ASJC Scopus subject areas
 Artificial Intelligence
Fields of Expertise
 Information, Communication & Computing
Treatment code (Nähere Zuordnung)
 Basic  Fundamental (Grundlagenforschung)
Documents & Links
Related content

Research Outputs

Big Data of Complex Networks
Research output: Book/Report › Book › peerreview