Graph mining refers to extracting knowledge from massive graphs. The data sets of telephone calls we see at AT&T can be viewed as a single graph, with several hundred million phone numbers as nodes, and calls between phone numbers as edges. It is a giant social network, like an internet connections graph or a rich citation network.
There are several semantic sources that can be found in the Web that are either explicit, e.g. Wikipedia, or implicit, e.g. derived from Web usage data. Most of them are related to user generated content (UGC) or what is called today the Web 2.0. In this talk we show several applications of mining the wisdom of crowds behind UGC to improve search. We will show live demos to find relations in the Wikipedia or to improve image search as well as our current research in the topic. Our final goal is to produce a virtuous data feedback circuit to leverage the Web itself.
by Andrew Moore (CMU), including tutorials on decision trees, information gain, cross validation, naive bayesian classifiers, hidden markov models, support vector machines, k-means and hierarchical clustering
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
Le blog de Stefan Bekier [Interprète de conférence, ancien militant de l'opposition de gauche en Pologne] 21 mai 2014: Volynets[ président du Syndicat indépendant des mineurs d'Ukraine (NPHU), également président de la Confédération ukrainienne des syndicats libres (KVPU),] explique : « Les séparatistes essaient d'arrêter le travail des groupes industriels comme Donbassanthracite, de porter un coup à l'économie, et, lorsque les mines s'arrêteront, de faire sortir les mineurs dans la rue. C'est fait pour aggraver le chaos et provoquer des troubles à l'est du pays. »
makeITfair-kampanjassa mukana olevat järjestöt Saksassa, Unkarissa, Hollannissa ja Ruotsissa luovuttivat vuoden aikana kerätyt kampanjakortit matkapuhelinoperaattoreille (KPN, T-Mobile, Tele2 ja Vodafone) 6.12. Tuhannet allekirjoitukset osoittavat, että r
Interests.
Database Systems, Data Mining, Statistical Modelling, Distributed Computing.
Saket joined IBM Research Australia in 2013 as a full-time researcher. He received a PhD degree in Computer Science from EPFL, Switzerland under Prof. Karl Aberer in March, 2013. At EPFL he was associated with the Distributed Information Systems Laboratory. Before that he received a Master's (M.Tech.) degree in Electrical Engineering from IIT Bombay in 2006. Prior to joining EPFL, he spent one year working for an Indian startup.