• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 1,423
Next 10 →

Query by Committee

by H. S. Seung, M. Opper, H. Sompolinsky , 1992
"... We propose an algorithm called query by committee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptr ..."
Abstract - Cited by 432 (3 self) - Add to MetaCart
We propose an algorithm called query by committee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another

Reflections on notecards: Seven issues for the next generation of hypermedia systems

by Frank G. Halasz - Communications of the ACM , 1988
"... NoteCards is a general hypermedia environment designed to help people work with ideas. Its intended users are authors, designers, and other intellectual laborers engaged in analyzing information, designing artifacts, and generally processing ideas. The system provides these users with a variety of h ..."
Abstract - Cited by 454 (2 self) - Add to MetaCart
of the major issues that must be addressed in the next generation of hypermedia systems. These seven issues are: search and query, composite nodes, virtual structures, computational engines, versioning, collaborative work, and tailorability. For each of these issues, the papers describes the limitations

TelegraphCQ: Continuous Dataflow Processing for an Uncertan World

by Sirish Chandrasekaran, Owen Cooper, Amol Deshpande, Michael J. Franklin, Joseph M. Hellerstein, Wei Hong, Sailesh Krishnamurthy, Sam Madden, Vijayshankar Raman, Fred Reiss, Mehul Shah , 2003
"... Increasingly pervasive networks are leading towards a world where data is constantly in motion. In such a world, conventional techniques for query processing, which were developed under the assumption of a far more static and predictable computational environment, will not be sufficient. Instead, qu ..."
Abstract - Cited by 514 (23 self) - Add to MetaCart
, query processors based on adaptive dataflow will be necessary. The Telegraph project has developed a suite of novel technologies for continuously adaptive query processing. The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams

Extracting Relations from Large Plain-Text Collections

by Eugene Agichtein, Luis Gravano , 2000
"... Text documents often contain valuable structured data that is hidden in regular English sentences. This data is best exploited if available as a relational table that we could use for answering precise queries or for running data mining tasks. We explore a technique for extracting such tables fr ..."
Abstract - Cited by 494 (25 self) - Add to MetaCart
Text documents often contain valuable structured data that is hidden in regular English sentences. This data is best exploited if available as a relational table that we could use for answering precise queries or for running data mining tasks. We explore a technique for extracting such tables

Property Testing and its connection to Learning and Approximation

by Oded Goldreich, Shafi Goldwasser, Dana Ron
"... We study the question of determining whether an unknown function has a particular property or is ffl-far from any function with that property. A property testing algorithm is given a sample of the value of the function on instances drawn according to some distribution, and possibly may query the fun ..."
Abstract - Cited by 475 (67 self) - Add to MetaCart
We study the question of determining whether an unknown function has a particular property or is ffl-far from any function with that property. A property testing algorithm is given a sample of the value of the function on instances drawn according to some distribution, and possibly may query

Improving automatic query expansion

by Ar Mitra Amit Singhal , 1998
"... Abstract Most casual users of IR systems type short queries. Recent research has shown that adding new words to these queries via odhoc feedback improves the re-trieval effectiveness of such queries. We investigate ways to improve this query expansion process by refining the set of documents used in ..."
Abstract - Cited by 286 (4 self) - Add to MetaCart
Abstract Most casual users of IR systems type short queries. Recent research has shown that adding new words to these queries via odhoc feedback improves the re-trieval effectiveness of such queries. We investigate ways to improve this query expansion process by refining the set of documents used

Protein homology detection by HMM-HMM comparison

by Johannes Söding - BIOINFORMATICS , 2005
"... Motivation: Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction, and evolution. Results: We have generalized the alignment of protein se-quences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile H ..."
Abstract - Cited by 401 (8 self) - Add to MetaCart
.7, and 3.3 times more good alignments (“balanced ” score> 0.3) than the next best method (COMPASS), and 1.6, 2.9, and 9.4 times more than PSI-BLAST, at the family, super-family, and fold level. Speed: HHsearch scans a query of 200 residues against 3691 domains in 33s on an AMD64 3GHz PC. This is 10

Shooting Stars in the Sky: An Online Algorithm for Skyline Queries

by Donald Kossmann, Frank Ramsak, Steffen Rost - In VLDB , 2002
"... Skyline queries ask for a set of interesting points from a potentially large set of data points. If we are traveling, for instance, a restaurant might be interesting if there is no other restaurant which is nearer, cheaper, and has better food. Skyline queries retrieve all such interesting restauran ..."
Abstract - Cited by 284 (0 self) - Add to MetaCart
Skyline queries ask for a set of interesting points from a potentially large set of data points. If we are traveling, for instance, a restaurant might be interesting if there is no other restaurant which is nearer, cheaper, and has better food. Skyline queries retrieve all such interesting

Querying the physical world

by Johannes Gehrke, Praveen Seshadri - IEEE Personal Communications , 2000
"... In the next decade, millions of sensors and small-scale mobile devices will integrate processors, memory and communication capabilities. Networks of devices will be widely deployed for monitoring applications. In these new applications, users need to query very large collections of devices in an ad- ..."
Abstract - Cited by 190 (1 self) - Add to MetaCart
In the next decade, millions of sensors and small-scale mobile devices will integrate processors, memory and communication capabilities. Networks of devices will be widely deployed for monitoring applications. In these new applications, users need to query very large collections of devices in an ad

Jena: Implementing the Semantic Web Recommendations

by Jeremy J. Carroll, Ian Dickinson, Chris Dollin, Andy Seaborne, Kevin Wilkinson, Dave Reynolds, Dave Reynolds , 2003
"... OWL have, at their heart, the RDF graph. Jena2, a secondgeneration RDF toolkit, is similarly centered on the RDF graph. RDFS and OWL reasoning are seen as graph-to-graph transforms, producing graphs of virtual triples. Rich APIs are provided. The Model API includes support for other aspects of the R ..."
Abstract - Cited by 261 (4 self) - Add to MetaCart
graphs can be stored in-memory or in databases. Jena's query language, RDQL, and the Web API are both offered for the next round of standardization.
Next 10 →
Results 1 - 10 of 1,423
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University