On the optimality of universal classifiers for finite-length individual test sequences

We consider pairs of finite-length individual sequences that are realizations of unknown, finite alphabet, stationary sources in a clas M of sources with vanishing memory (e.g. stationary Markov sources). The task of a universal classifier is to decide whether the two sequences are emerging from the same source or are emerging from two distinct sources in M, and it has to carry this task without any prior knowledge of the two underlying probability measures. Given a fidelity function and a fidelity criterion, the probability of classification error for a given universal classifier is defined. Two universal classifiers are defined for pairs of $N$ -sequence: A "classical" fixed-length (FL) universal classifier and an alternative variable-length (VL) universal classifier. Following Wyner and Ziv (1996) it is demonstrated that if the length of the individual sequences N is smaller than a cut-off value that is determined by the properties of the class M, any universal classifier will fail with high probability . It is demonstrated that for values of N larger than the cut-off rate, the classification error relative to either one of the two classifiers tends to zero as the length of the sequences tends to infinity. However, the probability of classification error that is associated with the variable-length universal classifier is uniformly smaller (or equal) to the one that is associated with the "classical" fixed-length universal classifier, for any finite length

On Finite Memory Universal Data Compression and Classification of Individual Sequences

Consider the case where consecutive blocks of N letters of a semi-infinite individual sequence X over a finite-alphabet are being compressed into binary sequences by some one-to-one mapping. No a-priori information about X is available at the encoder, which must therefore adopt a universal data-compression algorithm. It is known that if the universal LZ77 data compression algorithm is successively applied to N-blocks then the best error-free compression for the particular individual sequence X is achieved, as $N$ tends to infinity. The best possible compression that may be achieved by any universal data compression algorithm for finite N-blocks is discussed. It is demonstrated that context tree coding essentially achieves it. Next, consider a device called classifier (or discriminator) that observes an individual training sequence X. The classifier's task is to examine individual test sequences of length N and decide whether the test N-sequence has the same features as those that are captured by the training sequence X, or is sufficiently different, according to some appropriatecriterion. Here again, it is demonstrated that a particular universal context classifier with a storage-space complexity that is linear in N, is essentially optimal. This may contribute a theoretical "individual sequence" justification for the Probabilistic Suffix Tree (PST) approach in learning theory and in computational biology.Comment: The manuscrip was errneously replaced by a different one on a differnt topic, thus erasing the oricinal manuscrip