Jun 16, 2012 · We present learning of figures, nonempty compact sets in Euclidean space, based on Gold's learning model aiming at a computable foundation ...

Learning Figures with the Hausdorff. Metric by Fractals ... • Hausdorff distance between a figure K and a self-similar ... ) reliable learning, ) effective ...

We analyze learnable classes of figures from informants (positive and negative examples) and from texts (positive examples), and reveal the hierarchy of ...

We present learning of figures, nonempty compact sets in Euclidean space, based on Gold's learning model aiming at a computable foundation for binary ...

This paper amalgamate two processes: discretization and binary classification, based on Gold's learning model aiming at a computable foundation for binary ...

Request PDF | Learning Figures with the Hausdorff Metric by Fractals | Discretization is a fundamental process for machine learning from analog data such as ...

Angluin, D. (1980). Inductive inference of formal languages from positive data. Information and Control, 45(2), 117–135.

Discretization is a fundamental process for machine learning from analog data such as continuous signals. For example, the discrete Fourier analysis.

Bibliographic details on Learning Figures with the Hausdorff Metric by Fractals.

Article "Learning Figures with the Hausdorff Metric by Fractals" Detailed information of the J-GLOBAL is an information service managed by the Japan Science ...