Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Generalization bounds for learning weighted automata
AU - Balle, Borja
AU - Mohri, Mehryar
PY - 2018/3/15
Y1 - 2018/3/15
N2 - This paper studies the problem of learning weighted automata from a finite sample of strings with real-valued labels. We consider several hypothesis classes of weighted automata defined in terms of three different measures: the norm of an automaton's weights, the norm of the function computed by an automaton, and the norm of the corresponding Hankel matrix. We present new data-dependent generalization guarantees for learning weighted automata expressed in terms of the Rademacher complexity of these classes. We further present upper bounds on these Rademacher complexities, which reveal key new data-dependent terms related to the complexity of learning weighted automata.
AB - This paper studies the problem of learning weighted automata from a finite sample of strings with real-valued labels. We consider several hypothesis classes of weighted automata defined in terms of three different measures: the norm of an automaton's weights, the norm of the function computed by an automaton, and the norm of the corresponding Hankel matrix. We present new data-dependent generalization guarantees for learning weighted automata expressed in terms of the Rademacher complexity of these classes. We further present upper bounds on these Rademacher complexities, which reveal key new data-dependent terms related to the complexity of learning weighted automata.
KW - Learning theory
KW - Generalization bounds
KW - Weighted automata
KW - Rademacher complexity
U2 - 10.1016/j.tcs.2017.11.023
DO - 10.1016/j.tcs.2017.11.023
M3 - Journal article
VL - 716
SP - 89
EP - 106
JO - Theoretical Computer Science
JF - Theoretical Computer Science
SN - 0304-3975
ER -