Date/heure
Date(s) - 14 février 2014

Catégories Pas de Catégories

$Uniform\; stability\; and\; consistency\; for\; Operator\; valued\; kernel\; machines\; \backslash nby\; Julien\; Audiffren,\; LIF,\; Marseille.\backslash n\backslash nOperator-valued\; kernels,\; such\; as\; multi-task\; kernels,\; are\; appropriate\; for\; learning\; problems\; with\; nonscalar\; outputs\; like\; structured\; output\; prediction,\; and\; have\; interesting\; properties.\; For\; instance,\; they\; are\; particularly\; efficient\; for\; dealing\; with\; functional\; data,\; and\; for\; taking\; into\; account\; the\; structure\; of\; the\; output\; space.\; However,\; they\; have\; received\; little\; attention\; from\; the\; community,\; particularly\; from\; a\; theoretical\; point\; of\; vue.\; We\; will\; show\; that\; operator-valued\; kernel\; regression\; algorithms\; are\; uniformly\; stable\; in\; the\; general\; case\; of\; infinite-dimensional\; output\; spaces.\; We\; then\; derive\; under\; mild\; assumption\; on\; the\; kernel\; generalization\; bounds\; of\; such\; algorithms,\; and\; we\; show\; their\; consistency\; even\; with\; non\; Hilbert-Schmidt\; operator-valued\; kernels.[$

Date/heure
Date(s) - 14 février 2014

Catégories Pas de Catégories

$Uniform\; stability\; and\; consistency\; for\; Operator\; valued\; kernel\; machines\; \backslash nby\; Julien\; Audiffren,\; LIF,\; Marseille.\backslash n\backslash nOperator-valued\; kernels,\; such\; as\; multi-task\; kernels,\; are\; appropriate\; for\; learning\; problems\; with\; nonscalar\; outputs\; like\; structured\; output\; prediction,\; and\; have\; interesting\; properties.\; For\; instance,\; they\; are\; particularly\; efficient\; for\; dealing\; with\; functional\; data,\; and\; for\; taking\; into\; account\; the\; structure\; of\; the\; output\; space.\; However,\; they\; have\; received\; little\; attention\; from\; the\; community,\; particularly\; from\; a\; theoretical\; point\; of\; vue.\; We\; will\; show\; that\; operator-valued\; kernel\; regression\; algorithms\; are\; uniformly\; stable\; in\; the\; general\; case\; of\; infinite-dimensional\; output\; spaces.\; We\; then\; derive\; under\; mild\; assumption\; on\; the\; kernel\; generalization\; bounds\; of\; such\; algorithms,\; and\; we\; show\; their\; consistency\; even\; with\; non\; Hilbert-Schmidt\; operator-valued\; kernels.[$