Matches in SemOpenAlex for { <https://semopenalex.org/work/W1964223469> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W1964223469 endingPage "980" @default.
- W1964223469 startingPage "980" @default.
- W1964223469 abstract "In some domains (e.g., molecular biology), data repositories are large in size, dynamic, and physically distributed. Consequently, it is neither desirable nor feasible to gather all the data in a centralized location for analysis. Hence, efficient distributed learning algorithms that can operate across multiple data sources without the need to transmit large amounts of data and cumulative learning algorithms that can cope with data sets that grow at rapid rate are needed. The problem of learning from distributed data can be summarized as follows: data is distributed across multiple sites and the learner’s task is to discover useful knowledge from all the available data. For example, such knowledge might be expressed in the form of a decision tree or a set of rules for pattern classification. A distributed learning algorithm LD is said to be exact with respect to the hypothesis inferred by a learning algorithm L ,i fthe hypothesis produced by LD, using distributed data sets D1 through Dn is the same as that obtained by L when it is given access to the complete data set D, which can be constructed (in principle) by combining the individual data sets D1 through Dn. Our approach to distributed learning is based on a decomposition of the learning task into information extraction and hypothesis generation components. This involves identifying the information requirements of a learning algorithm and designing efficient means of providing the needed information to the hypothesis generation component, while avoiding the need to transmit large amounts of data. This offers a general strategy for transforming a batch or centralized learning algorithm into an exact distributed algorithm. In this approach to distributed learning, only the information extrac" @default.
- W1964223469 created "2016-06-24" @default.
- W1964223469 creator A5067341711 @default.
- W1964223469 date "2002-07-28" @default.
- W1964223469 modified "2023-09-24" @default.
- W1964223469 title "Learning in open-ended dynamic distributed environments" @default.
- W1964223469 cites W1582401051 @default.
- W1964223469 cites W1599111604 @default.
- W1964223469 doi "https://doi.org/10.5555/777092.777253" @default.
- W1964223469 hasPublicationYear "2002" @default.
- W1964223469 type Work @default.
- W1964223469 sameAs 1964223469 @default.
- W1964223469 citedByCount "0" @default.
- W1964223469 crossrefType "proceedings-article" @default.
- W1964223469 hasAuthorship W1964223469A5067341711 @default.
- W1964223469 hasConcept C113174947 @default.
- W1964223469 hasConcept C119857082 @default.
- W1964223469 hasConcept C120314980 @default.
- W1964223469 hasConcept C124101348 @default.
- W1964223469 hasConcept C130120984 @default.
- W1964223469 hasConcept C134306372 @default.
- W1964223469 hasConcept C154945302 @default.
- W1964223469 hasConcept C15744967 @default.
- W1964223469 hasConcept C162324750 @default.
- W1964223469 hasConcept C177264268 @default.
- W1964223469 hasConcept C187736073 @default.
- W1964223469 hasConcept C19417346 @default.
- W1964223469 hasConcept C199360897 @default.
- W1964223469 hasConcept C2779582901 @default.
- W1964223469 hasConcept C2780451532 @default.
- W1964223469 hasConcept C33923547 @default.
- W1964223469 hasConcept C41008148 @default.
- W1964223469 hasConcept C70061542 @default.
- W1964223469 hasConceptScore W1964223469C113174947 @default.
- W1964223469 hasConceptScore W1964223469C119857082 @default.
- W1964223469 hasConceptScore W1964223469C120314980 @default.
- W1964223469 hasConceptScore W1964223469C124101348 @default.
- W1964223469 hasConceptScore W1964223469C130120984 @default.
- W1964223469 hasConceptScore W1964223469C134306372 @default.
- W1964223469 hasConceptScore W1964223469C154945302 @default.
- W1964223469 hasConceptScore W1964223469C15744967 @default.
- W1964223469 hasConceptScore W1964223469C162324750 @default.
- W1964223469 hasConceptScore W1964223469C177264268 @default.
- W1964223469 hasConceptScore W1964223469C187736073 @default.
- W1964223469 hasConceptScore W1964223469C19417346 @default.
- W1964223469 hasConceptScore W1964223469C199360897 @default.
- W1964223469 hasConceptScore W1964223469C2779582901 @default.
- W1964223469 hasConceptScore W1964223469C2780451532 @default.
- W1964223469 hasConceptScore W1964223469C33923547 @default.
- W1964223469 hasConceptScore W1964223469C41008148 @default.
- W1964223469 hasConceptScore W1964223469C70061542 @default.
- W1964223469 hasLocation W19642234691 @default.
- W1964223469 hasOpenAccess W1964223469 @default.
- W1964223469 hasPrimaryLocation W19642234691 @default.
- W1964223469 hasRelatedWork W1499970328 @default.
- W1964223469 hasRelatedWork W152576908 @default.
- W1964223469 hasRelatedWork W1537612705 @default.
- W1964223469 hasRelatedWork W1553345567 @default.
- W1964223469 hasRelatedWork W1624504241 @default.
- W1964223469 hasRelatedWork W197787436 @default.
- W1964223469 hasRelatedWork W2039197307 @default.
- W1964223469 hasRelatedWork W2059869729 @default.
- W1964223469 hasRelatedWork W2084333448 @default.
- W1964223469 hasRelatedWork W2091801169 @default.
- W1964223469 hasRelatedWork W2151625787 @default.
- W1964223469 hasRelatedWork W2163061414 @default.
- W1964223469 hasRelatedWork W2169337357 @default.
- W1964223469 hasRelatedWork W2188736355 @default.
- W1964223469 hasRelatedWork W2238760083 @default.
- W1964223469 hasRelatedWork W2541884796 @default.
- W1964223469 hasRelatedWork W2542903344 @default.
- W1964223469 hasRelatedWork W3009947594 @default.
- W1964223469 hasRelatedWork W3047304572 @default.
- W1964223469 hasRelatedWork W3112020612 @default.
- W1964223469 isParatext "false" @default.
- W1964223469 isRetracted "false" @default.
- W1964223469 magId "1964223469" @default.
- W1964223469 workType "article" @default.