Autor(en): | Schwiegelshohn, Chris Sohler, Christian |
Titel: | Logistic Regression in Datastreams |
Sprache (ISO): | en |
Zusammenfassung: | 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. |
URI: | http://hdl.handle.net/2003/37168 http://dx.doi.org/10.17877/DE290R-19164 |
Erscheinungsdatum: | 2014-01 |
Enthalten in den Sammlungen: | Sonderforschungsbereich (SFB) 876 |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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schwiegelshohn_sohler_2014a.pdf | DNB | 354.08 kB | Adobe PDF | Öffnen/Anzeigen |
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