TY - JOUR
T1 - Maximal width learning of binary functions
AU - Anthony, Martin
AU - Ratsaby, Joel
PY - 2010/1/1
Y1 - 2010/1/1
N2 - This paper concerns learning binary-valued functions defined on R, and investigates how a particular type of 'regularity' of hypotheses can be used to obtain better generalization error bounds. We derive error bounds that depend on the sample width (a notion analogous to that of sample margin for real-valued functions). This motivates learning algorithms that seek to maximize sample width.
AB - This paper concerns learning binary-valued functions defined on R, and investigates how a particular type of 'regularity' of hypotheses can be used to obtain better generalization error bounds. We derive error bounds that depend on the sample width (a notion analogous to that of sample margin for real-valued functions). This motivates learning algorithms that seek to maximize sample width.
KW - Binary function classes
KW - Learning algorithms
UR - http://www.scopus.com/inward/record.url?scp=71749119513&partnerID=8YFLogxK
U2 - 10.1016/j.tcs.2009.09.020
DO - 10.1016/j.tcs.2009.09.020
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AN - SCOPUS:71749119513
SN - 0304-3975
VL - 411
SP - 138
EP - 147
JO - Theoretical Computer Science
JF - Theoretical Computer Science
IS - 1
ER -