Results 1  10
of
35
Unbounded Length Contexts for PPM
 The Computer Journal
, 1995
"... uses considerably greater computational resources (both time and space). The next section describes the basic PPM compression scheme. Following that we motivate the use of contexts of unbounded length, introduce the new method, and show how it can be implemented using a trie data structure. Then we ..."
Abstract

Cited by 111 (7 self)
 Add to MetaCart
uses considerably greater computational resources (both time and space). The next section describes the basic PPM compression scheme. Following that we motivate the use of contexts of unbounded length, introduce the new method, and show how it can be implemented using a trie data structure. Then we give some results that demonstrate an improvement of about 6% over the old method. Finally, a recentlypublished and seemingly unrelated compression scheme [2] is related to the unboundedcontext idea that forms the essential innovation of PPM*. 1 PPM: Prediction by partial match The basic idea of PPM is to use the last few characters in the input stream to predict the upcoming one. Models that condition their predictions on a few immediately preceding symbols are called "finitecontext" models of order k, where k is the number of preceding symbols used. PPM employs a suite of fixedorder context models with different values of k
On prediction using variable order Markov models
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2004
"... This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Cont ..."
Abstract

Cited by 56 (1 self)
 Add to MetaCart
This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average logloss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a “decomposed” CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the LempelZiv compression algorithm, significantly outperforms all algorithms on the protein classification problems.
A Compressionbased Algorithm for Chinese Word Segmentation
 Computational Linguistics
"... This paper describes a general scheme for segmenting text by inferring the position of word boundaries, thus supplying a necessary preprocessing step for applications like those mentioned above. Unlike other approaches, which involve a dictionary of legal words and are therefore languagespecific, i ..."
Abstract

Cited by 56 (7 self)
 Add to MetaCart
This paper describes a general scheme for segmenting text by inferring the position of word boundaries, thus supplying a necessary preprocessing step for applications like those mentioned above. Unlike other approaches, which involve a dictionary of legal words and are therefore languagespecific, it works by using a corpus of already segmented text for training and thus can easily be retargeted for any language for which a suitable corpus of segmented material is available. To infer word boundaries, a general adaptive text compression technique is used that predicts upcoming characters on the basis of their preceding context. Spaces are inserted into positions where their presence enables the text to be compressed more effectively. This approach means that we can capitalize on existing research in text compression to create good models for word segmentation. To build a segmenter for a new language, the only resource required is a corpus of segmented text to train the compression model...
Spam filtering using statistical data compression models
 Journal of Machine Learning Research
, 2006
"... Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. Since spam evolves continuously and most practical applications are based on online user feedback, the task call ..."
Abstract

Cited by 52 (12 self)
 Add to MetaCart
Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. Since spam evolves continuously and most practical applications are based on online user feedback, the task calls for fast, incremental and robust learning algorithms. In this paper, we investigate a novel approach to spam filtering based on adaptive statistical data compression models. The nature of these models allows them to be employed as probabilistic text classifiers based on characterlevel or binary sequences. By modeling messages as sequences, tokenization and other errorprone preprocessing steps are omitted altogether, resulting in a method that is very robust. The models are also fast to construct and incrementally updateable. We evaluate the filtering performance of two different compression algorithms; dynamic Markov compression and prediction by partial matching. The results of our empirical evaluation indicate that compression models outperform currently established spam filters, as well as a number of methods proposed in previous studies.
Text Mining: A new frontier for lossless compression
 In Data Compression Conference
, 1999
"... This paper aims to promote text compression as a key technology for text mining ..."
Abstract

Cited by 33 (6 self)
 Add to MetaCart
This paper aims to promote text compression as a key technology for text mining
Semantically Motivated Improvements for PPM Variants
 The Computer Journal
, 1997
"... This paper explains how to significantly improve the compression performance of any PPM variant ..."
Abstract

Cited by 25 (3 self)
 Add to MetaCart
This paper explains how to significantly improve the compression performance of any PPM variant
Switching between two universal source coding algorithms
 In Data Compression Conference
, 1998
"... This paper discusses a switching method which can be used to combine two sequential universal source coding algorithms. The switching method treats these two algorithms as blackboxes and can only use their estimates of the probability distributions for the consecutive symbols of the source sequence ..."
Abstract

Cited by 24 (1 self)
 Add to MetaCart
This paper discusses a switching method which can be used to combine two sequential universal source coding algorithms. The switching method treats these two algorithms as blackboxes and can only use their estimates of the probability distributions for the consecutive symbols of the source sequence. Three weighting algorithms based on this switching method are presented. Empirical results show that all three weighting algorithms give a performance better than the performance of the source coding algorithms they combine. 1
The Context Trees of Block Sorting Compression
 IN PROCEEDINGS OF THE IEEE DATA COMPRESSION CONFERENCE, SNOWBIRD, UTAH, MARCH 30  APRIL 1
, 1998
"... The BurrowsWheeler transform (BWT)andblock sorting compression are closely related to the context trees of PPM. The usual approach of treating BWT as merely a permutation is not able to fully exploit this relation. We show that ..."
Abstract

Cited by 20 (0 self)
 Add to MetaCart
The BurrowsWheeler transform (BWT)andblock sorting compression are closely related to the context trees of PPM. The usual approach of treating BWT as merely a permutation is not able to fully exploit this relation. We show that
OnLine Stochastic Processes in Data Compression
, 1996
"... The ability to predict the future based upon the past in finitealphabet sequences has many applications, including communications, data security, pattern recognition, and natural language processing. By Shannon's theory and the breakthrough development of arithmetic coding, any sequence, a 1 a 2 \ ..."
Abstract

Cited by 15 (6 self)
 Add to MetaCart
The ability to predict the future based upon the past in finitealphabet sequences has many applications, including communications, data security, pattern recognition, and natural language processing. By Shannon's theory and the breakthrough development of arithmetic coding, any sequence, a 1 a 2 \Delta \Delta \Delta a n , can be encoded in a number of bits that is essentially equal to the minimal informationlossless codelength, P i \Gamma log 2 p(a i ja 1 \Delta \Delta \Delta a i\Gamma1 ). The goal of universal online modeling, and therefore of universal data compression, is to deduce the model of the input sequence a 1 a 2 \Delta \Delta \Delta a n that can estimate each p(a i ja 1 \Delta \Delta \Delta a i\Gamma1 ) knowing only a 1 a 2 \Delta \Delta \Delta a i\Gamma1 so that the ex...
Spam filtering using compression models
, 2005
"... Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. Since spam evolves continuously and most practical applications are based on online user feedback, the task call ..."
Abstract

Cited by 15 (2 self)
 Add to MetaCart
Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. Since spam evolves continuously and most practical applications are based on online user feedback, the task calls for fast, incremental and robust learning algorithms. This paper summarizes our experiments for the TREC 2005 spam track, in which we consider the use of adaptive statistical data compression models for the spam filtering task. The nature of these models allows them to be employed as Bayesian text classifiers based on character sequences. Since messages are modeled as sequences of characters, tokenization and other errorprone preprocessing steps are omitted altogether, resulting in a method that is very robust. The models are also fast to construct and incrementally updateable. We present experimental results indicating that compression models perform well in comparison to established spam filters. We also show that the method is extremely robust to noise, which should make such filters difficult to defeat. 1