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ICML
2005
IEEE
14 years 9 months ago
Using additive expert ensembles to cope with concept drift
We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence ...
Jeremy Z. Kolter, Marcus A. Maloof
ICDM
2010
IEEE
122views Data Mining» more  ICDM 2010»
13 years 6 months ago
Learning Preferences with Millions of Parameters by Enforcing Sparsity
We study the retrieval task that ranks a set of objects for a given query in the pairwise preference learning framework. Recently researchers found out that raw features (e.g. word...
Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, Jaime G....
ICML
2006
IEEE
14 years 9 months ago
Online decoding of Markov models under latency constraints
The Viterbi algorithm is an efficient and optimal method for decoding linear-chain Markov Models. However, the entire input sequence must be observed before the labels for any tim...
Mukund Narasimhan, Paul A. Viola, Michael Shilman
TSMC
1998
99views more  TSMC 1998»
13 years 8 months ago
Learning visually guided grasping: a test case in sensorimotor learning
Abstract—We present a general scheme for learning sensorimotor tasks which allows rapid on-line learning and generalization of the learned knowledge to unfamiliar objects. The sc...
Ishay Kamon, Tamar Flash, Shimon Edelman
KDD
2007
ACM
149views Data Mining» more  KDD 2007»
14 years 9 months ago
Partial example acquisition in cost-sensitive learning
It is often expensive to acquire data in real-world data mining applications. Most previous data mining and machine learning research, however, assumes that a fixed set of trainin...
Victor S. Sheng, Charles X. Ling