TY  - JOUR
AB  - Besides the core learning algorithm itself, one major question in machine learning is how to best encode given training data such that the learning technology can efficiently learn based thereon and generalize to novel data. While classical approaches often rely on a hand coded data representation, the topic of autonomous representation or feature learning plays a major role in modern learning architectures. The goal of this contribution is to give an overview about different principles of autonomous feature learning, and to exemplify two principles based on two recent examples: autonomous metric learning for sequences, and autonomous learning of a deep representation for spoken language, respectively.
DA  - 2015
DO  - 10.1007/s13218-015-0372-1
KW  - Representation learning
KW  - Deep representation
KW  - Metric learning
KW  - Spoken language
LA  - eng
IS  - 4
M2  - 339–351
PY  - 2015
SN  - 0933-1875
SP  - 339–351-
T2  - KI - Künstliche Intelligenz
TI  - Autonomous Learning of Representations
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-27529558
Y2  - 2025-02-19T19:56:24
ER  -