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» GraphLab: A New Framework for Parallel Machine Learning
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AAAI
2007
13 years 10 months ago
Learning by Combining Observations and User Edits
We introduce a new collaborative machine learning paradigm in which the user directs a learning algorithm by manually editing the automatically induced model. We identify a generi...
Vittorio Castelli, Lawrence D. Bergman, Daniel Obl...
COLT
2008
Springer
13 years 9 months ago
On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms
Boosting algorithms build highly accurate prediction mechanisms from a collection of lowaccuracy predictors. To do so, they employ the notion of weak-learnability. The starting po...
Shai Shalev-Shwartz, Yoram Singer
BMCBI
2010
133views more  BMCBI 2010»
13 years 7 months ago
Improving de novo sequence assembly using machine learning and comparative genomics for overlap correction
Background: With the rapid expansion of DNA sequencing databases, it is now feasible to identify relevant information from prior sequencing projects and completed genomes and appl...
Lance E. Palmer, Mathäus Dejori, Randall A. B...
EMNLP
2011
12 years 7 months ago
Inducing Sentence Structure from Parallel Corpora for Reordering
When translating among languages that differ substantially in word order, machine translation (MT) systems benefit from syntactic preordering—an approach that uses features fro...
John DeNero, Jakob Uszkoreit
ECTEL
2007
Springer
14 years 1 months ago
Applying Sensemaking in a Mobile Learning Scenario
In this work, a new type of collaborative learning activity is proposed in order to enable students to explore and understand information in highly mobile situations. We call this ...
Gustavo Zurita, Nelson Baloian, Pedro Antunes, Fel...