Least Squares Revisited: Scalable Approaches for Multi-class Prediction

7 Oct 2013Alekh AgarwalSham M. KakadeNikos KarampatziakisLe SongGregory Valiant

This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large. These robust and parameter free algorithms are essentially iterative least-squares updates and very versatile both in theory and in practice... (read more)

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