On the learning of vague languages for syntactic pattern recognition

The method of the learning of vague languages which represent distorted/ambiguous patterns is proposed in the paper. The goal of the method is to infer the quasi-context-sensitive string grammar which is used in our model as the generator of patterns. The method is an important component of the multi-derivational model of the parsing of vague languages used for syntactic pattern recognition

Using genetic algorithms to create meaningful poetic text

Work carried out when all authors were at the University of Edinburgh.Peer reviewedPostprin

Preliminary experiments on human sensitivity to rhythmic structure in a grammar with recursive self-similarity

We present the first rhythm detection experiment using a Lindenmayer grammar, a self-similar recursive grammar shown previously to be learnable by adults using speech stimuli. Results show that learners were unable to correctly accept or reject grammatical and ungrammatical strings at the group level, although five (of 40) participants were able to do so with detailed instructions before the exposure phase

Big data and the SP theory of intelligence

This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" may, with advantage, be applied to the management and analysis of big data. The SP system -- introduced in the article and fully described elsewhere -- may help to overcome the problem of variety in big data: it has potential as "a universal framework for the representation and processing of diverse kinds of knowledge" (UFK), helping to reduce the diversity of formalisms and formats for knowledge and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualisation of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it.Comment: Accepted for publication in IEEE Acces

STARE: Spatio-Temporal Attention Relocation for multiple structured activities detection

We present a spatio-temporal attention relocation (STARE) method, an information-theoretic approach for efficient detection of simultaneously occurring structured activities. Given multiple human activities in a scene, our method dynamically focuses on the currently most informative activity. Each activity can be detected without complete observation, as the structure of sequential actions plays an important role on making the system robust to unattended observations. For such systems, the ability to decide where and when to focus is crucial to achieving high detection performances under resource bounded condition. Our main contributions can be summarized as follows: 1) information-theoretic dynamic attention relocation framework that allows the detection of multiple activities efficiently by exploiting the activity structure information and 2) a new high-resolution data set of temporally-structured concurrent activities. Our experiments on applications show that the STARE method performs efficiently while maintaining a reasonable level of accuracy

An automatic learning of grammar for syntactic pattern recognition, 1988

The practical utility of a syntactic pattern recognizer depends on an automatic learning of pattern class grammars from a sample of patterns. The basic idea is to devise a learning process based on induction of repeated subs rings. Several techniques based on formal lattice structures, structural derivatives, information, k tails, lattice structures, structural information sequence, inductive inference and heuristic approach are widely found in the literature. The purpose of this research is to first devise a minimal finite state automaton which recognizes all patterns. The automaton is then manipulated so that the induction of repetition is captured by cycles or loops. The final phase consists of converting the reduced automaton into a context - free grammar. Now, an automatic parser for this grammar can recognize patterns which are in the respective class