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» Boosting for transfer learning with multiple sources
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SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
11 years 8 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...
DAGM
2011
Springer
12 years 6 months ago
Multiple Instance Boosting for Face Recognition in Videos
For face recognition from video streams often cues such as transcripts, subtitles or on-screen text are available. This information could be very valuable for improving the recogni...
Paul Wohlhart, Martin Köstinger, Peter M. Rot...
ICCBR
2009
Springer
14 years 25 days ago
Case-Based Reasoning in Transfer Learning
Positive transfer learning (TL) occurs when, after gaining experience from learning how to solve a (source) task, the same learner can exploit this experience to improve performanc...
David W. Aha, Matthew Molineaux, Gita Sukthankar
EMNLP
2011
12 years 6 months ago
Multi-Source Transfer of Delexicalized Dependency Parsers
We present a simple method for transferring dependency parsers from source languages with labeled training data to target languages without labeled training data. We first demons...
Ryan T. McDonald, Slav Petrov, Keith Hall
ATAL
2009
Springer
14 years 25 days ago
Transfer via soft homomorphisms
The field of transfer learning aims to speed up learning across multiple related tasks by transferring knowledge between source and target tasks. Past work has shown that when th...
Jonathan Sorg, Satinder Singh