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» Linear Algorithms for Online Multitask Classification
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ICML
2009
IEEE
14 years 8 months ago
Online dictionary learning for sparse coding
Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
ML
2010
ACM
135views Machine Learning» more  ML 2010»
13 years 2 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
JMLR
2010
101views more  JMLR 2010»
13 years 2 months ago
Exploiting Feature Covariance in High-Dimensional Online Learning
Some online algorithms for linear classification model the uncertainty in their weights over the course of learning. Modeling the full covariance structure of the weights can prov...
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer...
JMLR
2006
116views more  JMLR 2006»
13 years 7 months ago
Step Size Adaptation in Reproducing Kernel Hilbert Space
This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SMD) algorithm to adapt its step size automatically. We formulate the online lear...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex ...
JMLR
2008
137views more  JMLR 2008»
13 years 7 months ago
Online Learning of Complex Prediction Problems Using Simultaneous Projections
We describe and analyze an algorithmic framework for online classification where each online trial consists of multiple prediction tasks that are tied together. We tackle the prob...
Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer