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ECAI
2008
Springer
15 years 8 months ago
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefre...
Qing Wang, Liang Zhang, Mingmin Chi, Jiankui Guo
ICDM
2009
IEEE
169views Data Mining» more  ICDM 2009»
15 years 4 months ago
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification
There are many clustering tasks which are closely related in the real world, e.g. clustering the web pages of different universities. However, existing clustering approaches neglec...
Quanquan Gu, Jie Zhou
248
Voted
SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
13 years 9 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
KDD
2008
ACM
161views Data Mining» more  KDD 2008»
16 years 7 months ago
Spectral domain-transfer learning
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, ...
ALT
2005
Springer
16 years 3 months ago
Learning Multiple Languages in Groups
We consider a variant of Gold’s learning paradigm where a learner receives as input n different languages (in form of one text where all input languages are interleaved). Our g...
Sanjay Jain, Efim B. Kinber