CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Understanding intelligence
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Spoken language analysis, modeling and recognition-statistical and adaptive connectionist approaches
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Integration of speech and vision using mutual information
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
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This paper describes efficient concept acquisition for an infant agent (IA) based on learning biases that are observed in child language development. An IA acquires concepts through learning relations between visual features of objects and acoustic features of human speech. In this task, the IA has to find out which visual features are indicated by a speech. Previous concept acquisition systems find out them by using probabilistic methods, however, such approaches need much samples to achieve high accuracy. In this paper, firstly, we propose basic concept acquisition system using Online-EM algorithm without the biases. And then, we implement two types of learning biases to accelerate a learning process into our system. The experimental results show that the proposed method can achieve efficient learning.