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» Query chains: learning to rank from implicit feedback
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ML
2006
ACM
15 years 3 months ago
Seminal: searching for ML type-error messages
We present a new way to generate type-error messages in a polymorphic, implicitly, and strongly typed language (specifically Caml). Our method separates error-message generation ...
Benjamin S. Lerner, Dan Grossman, Craig Chambers
AIRS
2006
Springer
15 years 1 months ago
Improving Re-ranking of Search Results Using Collaborative Filtering
Search Engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and the context of ...
U. Rohini, Vamshi Ambati
ICCV
2007
IEEE
15 years 3 months ago
Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which ha...
Ondrej Chum, James Philbin, Josef Sivic, Michael I...
IAAI
2011
13 years 9 months ago
NewsFinder: Automating an Artificial Intelligence News Service
NewsFinder automates the steps involved in finding, selecting and publishing news stories that meet subjective judgments of relevance and interest to the Artificial Intelligence c...
Liang Dong, Reid G. Smith, Bruce G. Buchanan
KDD
2004
ACM
210views Data Mining» more  KDD 2004»
15 years 10 months ago
Probabilistic author-topic models for information discovery
We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic pro...
Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T...