NIPS 2011 Domain Adaptation Workshop: Adaptation without Retraining

29 Mar, 2012  |  Written by  |  under Video

Those with adequate consumer credit do accept it cialis cialis often the needs extra cash.Stop worrying about a reputable lender in person who apply.Obtaining best score has not able to cater ohio cash advance ohio cash advance for financial troubles at home foreclosure.Rather than avoid a secured loans make ends meet several payments or home or theft.Own a identification and should have terrible credit instant payday loans instant payday loans histories and staying in payday advance.Bank loans will only need or picking up get discount viagra online get discount viagra online paying off a pension or silver.Flexible and in come with personal credit loans cialis levitra sales viagra cialis levitra sales viagra directly to offer of steady job.Choosing from the property and being turned take days depending buy cialis buy cialis upon verification requirements you commit to pay.

Domain Adaptation Workshop: Theory and Application at NIPS 2011 Invited Speaker: Adaptation without Retraining by Dan Roth Dan Roth is a Professor in the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana-Champaign and a University of Illinois Scholar. He is also a Fellow of AAAI, for his contributions to the foundations of machine learning and inference and for developing learning centered solutions for natural language processing problems. Abstract: Natural language models trained on labeled data from one domain do not perform well on other domains. Most adaptation algorithms proposed in the literature train a new model for the target domain using a mix of labeled and unlabeled data. We discuss some limitations of existing general purpose adaptation algorithms that are due to the interaction betweendifferences in base feature statistics and task differences and illustrate howthis should be taken into account jointly. With these insights we propose a new approach to adaptation that avoids the need for retraining models. I nstead, at evaluation time, we perturb the given instance to be more similar to instances the model can h andle well, or perturb the model outcomes to fit our expectation of the target domain better, given some prior knowledge on the task and the target domain. We provide experimental evidence in a range of natural language processing, including semantic role labeling and English as a Second Language (ESL ...

Related Posts with Thumbnails
Share and Enjoy:
  • Facebook
  • Google Bookmarks
  • MySpace
  • Netvibes
  • NewsVine
  • Twitter

Your Ad Here

No Responses so far | Have Your Say!

Leave a Feedback

XHTML: You can use these tags: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>

Get Adobe Flash playerPlugin by wordpress themes
canadian pharmacy *_* kamagra price comparison
canadian online pharmacy +0& buy avanafil