We explain how to apply statistical techniques to solve several language-recognition problems that arise in cryptanalysis and other domains. Language recognition is important in cryptanal-ysis because, among other applications, an exhaustive key search of any cryptosystem from ciphertext alone requires a test that recognizes valid plaintext. Written for cryptanalysts, this guide should also be ...

Observing the environment and recoganizing patterns for the purpose of decision making are fundamental to any scientific enquiry. Pattern recognition is a scientific discipline so much so that it enables perception in machines and also it has applications in diverse technology areas. Among the scientific community, statistical pattern recognition has received considerable attention in recent ye...

Recent developments in statistics now allow maximum likelihood estimators for the parameters of Markov Random Fields to be constructed. We detail the theory required, and present an algorithm which is easily implemented and practical in terms of computation time. We demonstrate this algorithm on three MRF models, the standard Potts model, an inhomogeneous variation of the Potts model and a long...

In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat c...

In this paper a new version of a bank-check processing system is presented. Many important integrations are proposed in order to improve its classification accuracy. This goal is achieved by using the Computer Aided Software Engineering tools provided by the "Khoros" software framework. New algorithms, based on Hidden Markov Models, both for courtesy and legal amount recognition, are implemente...

Hidden Markov Models are one of the most popular and successful techniques used in statistical pattern recognition. However, they are not well understood on a fundamental level. For example, we do not know how to characterize the class of processes that can be well approximated by HMMs. This thesis tries to uncover the source of the intrinsic expressiveness of HMMs by studying when and why two ...

gesture and motion recognition are needed for a variety of applications. the use of human hand motions as a natural interface tool has motivated researchers to conduct research in the modeling, analysis and recognition of various hand movements. in particular, human-computer intelligent interaction has been a focus of research in vision-based gesture recognition. in this work, we introduce a 3-...

Within computational linguistics, the use of statistical pattern matching is generally restricted to speech processing. We have attempted to apply statistical techniques to discover a grammatical classification system from a Corpus of 'raw' English text. A discovery procedure is simpler for a simpler language model; we assume a first-order Markov model, which (surprisingly) is shown elsewhere t...

This paper describes a strategy on learning from time series data and on using learned model for forecasting. Time series forecasting, which analyzes and predicts a variable changing over time, has received much attention due to its use for forecasting stock prices, but it can also be used for pattern recognition and data mining. Our method for learning from time series data consists of detecti...