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TKDE
2008
123views more  TKDE 2008»
15 years 6 months ago
Explaining Classifications For Individual Instances
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...
Marko Robnik-Sikonja, Igor Kononenko
COLING
2010
15 years 1 months ago
An Empirical Study on Web Mining of Parallel Data
This paper1 presents an empirical approach to mining parallel corpora. Conventional approaches use a readily available collection of comparable, nonparallel corpora to extract par...
Gum-Won Hong, Chi-Ho Li, Ming Zhou, Hae-Chang Rim
EMNLP
2011
14 years 6 months ago
Training dependency parsers by jointly optimizing multiple objectives
We present an online learning algorithm for training parsers which allows for the inclusion of multiple objective functions. The primary example is the extension of a standard sup...
Keith Hall, Ryan T. McDonald, Jason Katz-Brown, Mi...
ICIP
2005
IEEE
16 years 8 months ago
A complementary SVMs-based image annotation system
A novel automatic image annotation system is proposed, which integrates two sets of SVMs (Support Vector Machines), namely the MIL-based (Multiple Instance Learning) and global-fe...
Yutao Han, Xiaojun Qi
TIT
2002
164views more  TIT 2002»
15 years 6 months ago
On the generalization of soft margin algorithms
Generalization bounds depending on the margin of a classifier are a relatively recent development. They provide an explanation of the performance of state-of-the-art learning syste...
John Shawe-Taylor, Nello Cristianini
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