Learning Complexity Dimensions for a Continuous-Time Control System
<|reference_start|>Learning Complexity Dimensions for a Continuous-Time Control System: This paper takes a computational learning theory approach to a problem of linear systems identification. It is assumed that input signals have only a finite number k of frequency components, and systems to be identified have dimension no greater than n. The main result establishes that the sample complexity needed for identification scales polynomially with n and logarithmically with k.<|reference_end|>
@article{kuusela2000learning,
    title={Learning Complexity Dimensions for a Continuous-Time Control System},
    author={Pirkko Kuusela, Daniel Ocone, Eduardo D. Sontag (Rutgers, The State
  University of New Jersey, USA)},
    journal={arXiv preprint arXiv:math/0012163},
    year={2000},
    archivePrefix={arXiv},
    eprint={math/0012163},
    primaryClass={math.OC cs.LG}
}