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» Incremental Dependency Parsing Using Online Learning
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VLDB
2001
ACM
190views Database» more  VLDB 2001»
14 years 2 days ago
LEO - DB2's LEarning Optimizer
Most modern DBMS optimizers rely upon a cost model to choose the best query execution plan (QEP) for any given query. Cost estimates are heavily dependent upon the optimizer’s e...
Michael Stillger, Guy M. Lohman, Volker Markl, Mok...
ICDM
2009
IEEE
160views Data Mining» more  ICDM 2009»
14 years 2 months ago
Fast Online Training of Ramp Loss Support Vector Machines
—A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMR s) is proposed. It finds the optimal SVMR for t+1 training examples using SVMR built on t...
Zhuang Wang, Slobodan Vucetic
PKDD
2010
Springer
183views Data Mining» more  PKDD 2010»
13 years 6 months ago
Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes
Abstract. In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an i...
Zhao Xu, Kristian Kersting, Thorsten Joachims
SDM
2009
SIAM
119views Data Mining» more  SDM 2009»
14 years 4 months ago
Twin Vector Machines for Online Learning on a Budget.
This paper proposes Twin Vector Machine (TVM), a constant space and sublinear time Support Vector Machine (SVM) algorithm for online learning. TVM achieves its favorable scaling b...
Zhuang Wang, Slobodan Vucetic
CVPR
2012
IEEE
11 years 10 months ago
The use of on-line co-training to reduce the training set size in pattern recognition methods: Application to left ventricle seg
The use of statistical pattern recognition models to segment the left ventricle of the heart in ultrasound images has gained substantial attention over the last few years. The mai...
Gustavo Carneiro, Jacinto C. Nascimento