{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T13:25:23Z","timestamp":1648646723438},"reference-count":0,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[1996,5]]},"abstract":" Learning from examples has a number of distinct algebraic forms, depending on what is to be learned from the available information. One of these forms is [Formula: see text], where the input-output tuple (x, y) is the available information, and G represents the process determining the mapping from x to y. Various models, y = f(x), of G can be constructed using the information from the (x, y) tuples. In general, and for real-world problems, it is not reasonable to expect the exact representation of G to be found (i.e. a formula that is correct for all possible (x, y)). The modeling procedure involves finding a satisfactory set of basis functions, their combination, a coding for (x, y) and then to adjust all free parameters in an approximation process, to construct a final model. The approximation process can bring the accuracy of the model to a certain level, after which it becomes increasingly expensive to improve further. Further improvement may be gained through constructing a number of agents {\u03b1}, each of which develops its own model f\u03b1<\/jats:sub>. These may then be combined in a second modeling phase to synthesize a team model. If each agent has the ability for internal reflection the combination in a team framework becomes more profitable. We describe reflection and the generation of a confidence function: the agent's estimate of the correctness of each of its predictions. The presence of reflective information is shown to increase significantly the performance of a team. <\/jats:p>","DOI":"10.1142\/s0218001496000190","type":"journal-article","created":{"date-parts":[[2004,9,6]],"date-time":"2004-09-06T11:50:09Z","timestamp":1094471409000},"page":"251-272","source":"Crossref","is-referenced-by-count":5,"title":["LEARNING FROM EXAMPLES, AGENT TEAMS AND THE CONCEPT OF REFLECTION"],"prefix":"10.1142","volume":"10","author":[{"given":"UWE","family":"BEYER","sequence":"first","affiliation":[{"name":"German National Research Centre for Computer Science (GMD), Schlo\u00df Birlinghoven, 53754 St. Augustin, Germany"}]},{"given":"FRANK J.","family":"\u015aMIEJA","sequence":"additional","affiliation":[{"name":"German National Research Centre for Computer Science (GMD), Schlo\u00df Birlinghoven, 53754 St. Augustin, Germany"}]}],"member":"219","published-online":{"date-parts":[[2012,4,30]]},"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001496000190","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T13:03:38Z","timestamp":1565183018000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001496000190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1996,5]]},"references-count":0,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2012,4,30]]},"published-print":{"date-parts":[[1996,5]]}},"alternative-id":["10.1142\/S0218001496000190"],"URL":"https:\/\/doi.org\/10.1142\/s0218001496000190","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[1996,5]]}}}