Least Squares Revisited: Scalable Approaches for Multi-class Prediction

  title={Least Squares Revisited: Scalable Approaches for Multi-class Prediction},
  author={Alekh Agarwal and Sham M. Kakade and Nikos Karampatziakis and Le Song and Gregory Valiant},
This work provides simple algorithms for multiclass (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. On the theoretical front, we present several variants with convergence guarantees. Owing to their effective use of second-order structure, these algorithms are substantially better… CONTINUE READING
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Least squares revisited: Scalable approaches for multi-class prediction

  • A. Agarwal, S. M. Kakade, N. Karampatziakis, L. Song, G. Valiant
  • arXiv preprint arXiv:1310.1949,
  • 2013
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