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NIPS
2007
13 years 11 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
CIKM
2009
Springer
14 years 5 months ago
Incremental query evaluation for support vector machines
Support vector machines (SVMs) have been widely used in multimedia retrieval to learn a concept in order to find the best matches. In such a SVM active learning environment, the ...
Danzhou Liu, Kien A. Hua
LREC
2008
112views Education» more  LREC 2008»
13 years 11 months ago
Linguistic Structure and Bilingual Informants Help Induce Machine Translation of Lesser-Resourced Languages
Producing machine translation (MT) for the many minority languages in the world is a serious challenge. Minority languages typically have few resources for building MT systems. Fo...
Christian Monson, Ariadna Font Llitjós, Vam...
LREC
2008
155views Education» more  LREC 2008»
13 years 11 months ago
Using Reordering in Statistical Machine Translation based on Alignment Block Classification
Statistical Machine Translation (SMT) is based on alignment models which learn from bilingual corpora the word correspondences between source and target language. These models are...
Marta R. Costa-Jussà, José A. R. Fon...
GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
14 years 3 months ago
GAMM: genetic algorithms with meta-models for vision
Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an ex...
Greg Lee, Vadim Bulitko