{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T20:10:36Z","timestamp":1739995836365,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008]]},"abstract":"<jats:p>This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for the case of small-scale and large-scale learning problems. Small-scale learning problems are subject to the usual approximation&amp;ndash;estimation tradeoff. Large-scale learning problems are subject to a qualitatively different tradeoff involving the computational complexity of the underlying optimization algorithms in non-trivial ways. For instance, a mediocre optimization algorithms, stochastic gradient descent, is shown to perform very well on large-scale learning problems.<\/jats:p>","DOI":"10.3233\/978-1-58603-898-4-15","type":"book-chapter","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T19:11:46Z","timestamp":1739992306000},"source":"Crossref","is-referenced-by-count":0,"title":["Learning using Large Datasets"],"prefix":"10.3233","author":[{"family":"Bottou L&eacute;on","sequence":"additional","affiliation":[]},{"family":"Bousquet Olivier","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["NATO Science for Peace and Security Series - D: Information and Communication Security","Mining Massive Data Sets for Security"],"original-title":[],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T19:29:27Z","timestamp":1739993367000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=1874-6268&volume=19&spage=15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-58603-898-4-15","relation":{},"ISSN":["1874-6268"],"issn-type":[{"value":"1874-6268","type":"print"}],"subject":[],"published":{"date-parts":[[2008]]}}}