An efficient projection for l1,∞ regularization
In recent years the l[subscript 1],[subscript infinity] norm has been proposed for joint regularization. In essence, this type of regularization aims at extending the l[subscript 1] framework for learning sparse models to a setting where the goal is to learn a set of jointly sparse models. In this p...
Main Authors: | Quattoni, Ariadna, Carreras Perez, Xavier, Collins, Michael |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | en_US |
Published: |
Association for Computing Machinery
2010
|
Subjects: | |
Online Access: | http://hdl.handle.net/1721.1/59367 |
Similar Items
-
A Projected Subgradient Method for Scalable Multi-Task Learning
by: Quattoni, Ariadna, et al.
Published: (2008) -
A latent variable ranking model for content-based retrieval
by: Quattoni, Ariadna, et al.
Published: (2012) -
Non-Projective Parsing for Statistical Machine Translation
by: Carreras Perez, Xavier, et al.
Published: (2010) -
Reinforcement Learning for Mapping Instructions to Actions
by: Branavan, Satchuthanan R., et al.
Published: (2010) -
Unsupervised multilingual grammar induction
by: Snyder, Benjamin, et al.
Published: (2010)