Tracking a Small Set of Experts by Mixing Past Posteriors
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Trees, Windows, and Tiles for Wavelet Image Compression
DCC '00 Proceedings of the Conference on Data Compression
DCC '00 Proceedings of the Conference on Data Compression
Tracking a small set of experts by mixing past posteriors
The Journal of Machine Learning Research
IEEE Transactions on Signal Processing
Discrete denoising with shifts
IEEE Transactions on Information Theory
Universal randomized switching
IEEE Transactions on Signal Processing
A novel adaptive diversity achieving channel estimation scheme for LTE
Proceedings of the first ACM international workshop on Practical issues and applications in next generation wireless networks
Tracking the best level set in a level-crossing analog-to-digital converter
Digital Signal Processing
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Two weighting procedures are presented for compaction of output sequences generated by binary independent sources whose unknown parameter may occasionally change. The resulting codes need no knowledge of the sequence length T, i.e., they are strongly sequential, and also the number of parameter changes is unrestricted. The additional-transition redundancy of the first method was shown to achieve the Merhav lower bound, i.e., log T bits per transition. For the second method we could prove that additional-transition redundancy is not more than 3/2 log T bits per transition, which is more than the Merhav bound; however, the storage and computational complexity of this method are also more interesting than those of the first method. Simulations show that the difference in redundancy performance between the two methods is negligible