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info.json
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{
"abstract": "We give dimension-free and data-dependent bounds for linear multi-task\nlearning where a common linear operator is chosen to preprocess data for a\nvector of task specific linear-thresholding classifiers. The complexity\npenalty of multi-task learning is bounded by a simple expression involving\nthe margins of the task-specific classifiers, the Hilbert-Schmidt norm of\nthe selected preprocessor and the Hilbert-Schmidt norm of the covariance\noperator for the total mixture of all task distributions, or, alternatively,\nthe Frobenius norm of the total Gramian matrix for the data-dependent\nversion. The results can be compared to state-of-the-art results on linear\nsingle-task learning.",
"authors": [
"Andreas Maurer"
],
"id": "maurer06a",
"issue": 5,
"pages": [
117,
139
],
"title": "Bounds for Linear Multi-Task Learning",
"volume": "7",
"year": "2006"
}