{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T19:35:29Z","timestamp":1760297729128},"reference-count":17,"publisher":"World Scientific Pub Co Pte Lt","issue":"07","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2006,11]]},"abstract":"<jats:p> AdaBoost rarely suffers from overfitting problems in low noise data cases. However, recent studies with highly noisy patterns have clearly shown that overfitting can occur. A natural strategy to alleviate the problem is to penalize the data distribution skewness in the learning process to prevent several hardest examples from spoiling decision boundaries. In this paper, we pursue such a penalty scheme in the mathematical programming setting, which allows us to define a suitable classifier soft margin. By using two smooth convex penalty functions, based on Kullback\u2013Leibler divergence (KL) and l<jats:sub>2<\/jats:sub> norm, we derive two new regularized AdaBoost algorithms, referred to as AdaBoost<jats:sub>KL<\/jats:sub> and AdaBoost<jats:sub>Norm2<\/jats:sub>, respectively. We prove that our algorithms perform stage-wise gradient descent on a cost function, defined in the domain of their associated soft margins. We demonstrate the effectiveness of the proposed algorithms through experiments over a wide variety of data sets. Compared with other regularized AdaBoost algorithms, our methods achieve at least the same or better performance. <\/jats:p>","DOI":"10.1142\/s0218001406005137","type":"journal-article","created":{"date-parts":[[2006,12,27]],"date-time":"2006-12-27T06:49:45Z","timestamp":1167202185000},"page":"1093-1116","source":"Crossref","is-referenced-by-count":32,"title":["REDUCING THE OVERFITTING OF ADABOOST BY CONTROLLING ITS DATA DISTRIBUTION SKEWNESS"],"prefix":"10.1142","volume":"20","author":[{"given":"YIJUN","family":"SUN","sequence":"first","affiliation":[{"name":"Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL 32611-6130, USA"},{"name":"Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611-6130, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"SINISA","family":"TODOROVIC","sequence":"additional","affiliation":[{"name":"3021 Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL 61801, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JIAN","family":"LI","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611-6130, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2012,4,30]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1162\/089976699300016106"},{"key":"rf2","first-page":"273","volume":"20","author":"Cortes C.","journal-title":"Mach. 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