Quantifying accuracy of learning via sample width

Martin Anthony, Joel Ratsaby

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

In a recent paper, the authors introduced the notion of sample width for binary classifiers defined on the set of real numbers. It was shown that the performance of such classifiers could be quantified in terms of this sample width. This paper considers how to adapt the idea of sample width so that it can be applied in cases where the classifiers are defined on some finite metric space. We discuss how to employ a greedy set-covering heuristic to bound generalization error. Then, by relating the learning problem to one involving certain graph-theoretic parameters, we obtain generalization error bounds that depend on the sample width and on measures of 'density' of the underlying metric space.

اللغة الأصليةإنجليزيّة أمريكيّة
عنوان منشور المضيفProceedings of the 2013 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
الصفحات84-90
عدد الصفحات7
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 15 أكتوبر 2013
الحدث2013 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, سنغافورة
المدة: ١٦ أبريل ٢٠١٣١٩ أبريل ٢٠١٣

سلسلة المنشورات

الاسمProceedings of the 2013 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

!!Conference

!!Conference2013 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
الدولة/الإقليمسنغافورة
المدينةSingapore
المدة١٦/٠٤/١٣١٩/٠٤/١٣

All Science Journal Classification (ASJC) codes

  • !!Artificial Intelligence

بصمة

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