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» Semi-supervised Learning from General Unlabeled Data
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DIS
2009
Springer
14 years 2 months ago
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
Yasser El-Manzalawy, Vasant Honavar
KDD
2010
ACM
245views Data Mining» more  KDD 2010»
13 years 9 months ago
Learning incoherent sparse and low-rank patterns from multiple tasks
We consider the problem of learning incoherent sparse and lowrank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the spa...
Jianhui Chen, Ji Liu, Jieping Ye
KDD
2000
ACM
153views Data Mining» more  KDD 2000»
13 years 11 months ago
The generalized Bayesian committee machine
In this paper we introduce the Generalized Bayesian Committee Machine (GBCM) for applications with large data sets. In particular, the GBCM can be used in the context of kernel ba...
Volker Tresp
JMLR
2006
131views more  JMLR 2006»
13 years 7 months ago
On Representing and Generating Kernels by Fuzzy Equivalence Relations
Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of l...
Bernhard Moser
CSIE
2009
IEEE
14 years 2 months ago
Building a General Purpose Cross-Domain Sentiment Mining Model
Building a model using machine learning that can classify the sentiment of natural language text often requires an extensive set of labeled training data from the same domain as t...
Matthew Whitehead, Larry Yaeger