Logistic Regression in Datastreams
| dc.contributor.author | Schwiegelshohn, Chris | |
| dc.contributor.author | Sohler, Christian | |
| dc.date.accessioned | 2018-10-12T08:23:06Z | |
| dc.date.available | 2018-10-12T08:23:06Z | |
| dc.date.issued | 2014-01 | |
| dc.description.abstract | Learning from data streams is a well researched task both in theory and practice. As remarked by Clarkson, Hazan and Woodruff, many classification problems cannot be very well solved in a streaming setting. For previous model assumptions, there exist simple, yet highly artificial lower bounds prohibiting space efficient one- pass algorithms. At the same time, several classification algorithms are often successfully used in practice. To overcome this gap, we give a model relaxing the constraints that previously made classification impossible from a theoretical point of view and under these model assumptions provide the first (1 + epsilon) -approximate algorithms for sketching the objective values of logistic regression and perceptron classifiers in data streams. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/37168 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-19164 | |
| dc.language.iso | en | de |
| dc.relation.ispartofseries | Technical report / Sonderforschungsbereich Verfügbarkeit von Information durch Analyse unter Ressourcenbeschränkung;1/2014 | en |
| dc.subject.ddc | 004 | |
| dc.title | Logistic Regression in Datastreams | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | de |
| dcterms.accessRights | open access | |
| eldorado.secondarypublication | false | de |