COLT'05 Proceedings of the 18th annual conference on Learning Theory
Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms
The Journal of Machine Learning Research
Optimization and Convergence of Observation Channels in Stochastic Control
SIAM Journal on Control and Optimization
| Hi-index | 754.84 |
The in-training-set performance of a vector quantizer as a function of its training set size is investigated. For squared error distortion and independent training data, worst case type upper bounds are derived on the minimum training distortion achieved by an empirically optimal quantizer. These bounds show that the training distortion can underestimate the minimum distortion of a truly optimal quantizer by as much as a constant times n-1/2, where n is the size of the training data. Earlier results provide lower bounds of the same order