The “Prediction by Partial Matching” (PPM) data compression algorithm developed by Cleary and Witten is capable of very high compression rates, encoding English text in as little as 2.2 bits/character. Here it is shown that the estimates made by Cleary and Witten of the resources required to implement the scheme can be revised to allow for a tractable and useful implementation. In particular, a...

We describe an efficient algorithm for visual landmark recognition based on elastic template matching. Each landmark is represented by a set of features and their spatial relations. The matching procedure allows for visual transformations such as translation and expansion, or even partial occlusion.

The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches in terms of prediction accuracy on a range of branch difference predict...

Text classification is one of the most actual among the natural language processing problems. In this paper the application of word-based PPM (Prediction by Partial Matching) model for automatic content-based text classification is described. Our main idea is that words and especially word combinations are more relevant features for many text classification tasks. Key-words for a document in mo...

It has been demonstrated with recent software implementations of context modeling the capability of PPM (Prediction by Partial Matching) [1] type of algorithms to achieve very high compression rates. However, the cost is high in terms of computational complexity and low speed. Hardware implementations of compression algorithms are capable of increasing compression speed by at least an order of ...

The main aim of this thesis is to build adaptive language models of Arabic text that can achieve the best compression performance over existing models. Prediction by partial matching (PPM) language models has been the best performing over the other adaptive language models through the past three decades in term of compression performance. In order to get such performance for Arabic text, the ri...