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AR
2011
14 years 4 months ago
Learning, Generation and Recognition of Motions by Reference-Point-Dependent Probabilistic Models
This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using referenc...
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Sa...
ICASSP
2011
IEEE
14 years 1 months ago
Multi-view and multi-objective semi-supervised learning for large vocabulary continuous speech recognition
Current hidden Markov acoustic modeling for large vocabulary continuous speech recognition (LVCSR) relies on the availability of abundant labeled transcriptions. Given that speech...
Xiaodong Cui, Jing Huang, Jen-Tzung Chien
RECOMB
2004
Springer
15 years 10 months ago
Learning Regulatory Network Models that Represent Regulator States and Roles
Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
Keith Noto, Mark Craven
WEBI
2009
Springer
15 years 4 months ago
Learning Deep Web Crawling with Diverse Features
—The key to Deep Web crawling is to submit promising keywords to query form and retrieve Deep Web content efficiently. To select keywords, existing methods make a decision based ...
Lu Jiang, Zhaohui Wu, Qinghua Zheng, Jun Liu
SARA
2005
Springer
15 years 3 months ago
Learning Regular Expressions from Noisy Sequences
Abstract. The presence of long gaps dramatically increases the difficulty of detecting and characterizing complex events hidden in long sequences. In order to cope with this proble...
Ugo Galassi, Attilio Giordana