1553 search results - page 174 / 311 » Learning from Multiple Sources of Inaccurate Data |
144
Voted
ECCV
2008
Springer
16 years 5 months ago
2008
Springer
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
123
Voted
SAC
2008
ACM
15 years 3 months ago
2008
ACM
Ontology learning integrates many complementary techniques, including machine learning, natural language processing, and data mining. Specifically, clustering techniques facilitat...
149
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ATAL
2010
Springer
15 years 3 months ago
2010
Springer
We introduce the novel problem of inter-robot transfer learning for perceptual classification of objects, where multiple heterogeneous robots communicate and transfer learned obje...
140
click to vote
SIGMOD
2002
ACM
16 years 3 months ago
2002
ACM
XML is widely recognized as the data interchange standard for tomorrow, because of its ability to represent data from a wide variety of sources. Hence, XML is likely to be the for...
161
Voted
JMLR
2011
14 years 10 months ago
2011
We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an...