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» The Hardness of Metric Labeling
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LREC
2010
164views Education» more  LREC 2010»
14 years 11 months ago
Evaluating Machine Translation Utility via Semantic Role Labels
We present the methodology that underlies new metrics for semantic machine translation evaluation that we are developing. Unlike widely-used lexical and n-gram based MT evaluation...
Chi-kiu Lo, Dekai Wu
ISDA
2008
IEEE
15 years 3 months ago
Improving VG-RAM WNN Multi-label Text Categorization via Label Correlation
In multi-label text databases one or more labels, or categories, can be assigned to a single document. In many such databases there can be correlation on the assignment of subsets...
Alberto Ferreira de Souza, Claudine Badue, Bruno Z...
FOCS
2009
IEEE
15 years 4 months ago
Agnostic Learning of Monomials by Halfspaces Is Hard
— We prove the following strong hardness result for learning: Given a distribution on labeled examples from the hypercube such that there exists a monomial (or conjunction) consi...
Vitaly Feldman, Venkatesan Guruswami, Prasad Ragha...
FOCS
2006
IEEE
15 years 3 months ago
Hardness of Learning Halfspaces with Noise
Learning an unknown halfspace (also called a perceptron) from labeled examples is one of the classic problems in machine learning. In the noise-free case, when a halfspace consist...
Venkatesan Guruswami, Prasad Raghavendra
CVPR
2009
IEEE
16 years 4 months ago
Learning a Distance Metric from Multi-instance Multi-label Data
Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. ...
Rong Jin (Michigan State University), Shijun Wang...