Pattern Recognition and Machine Intelligence

Abstract The classical learning problem of the pattern recognition in a finite-dimensional linear space of real-valued features is studied under the conditions of a non-stationary universe. The training criterion of non-stationary pattern recognition is formulated as a generalization of the classical Support Vector Machine. The respective numerical algorithm has the computation complexity proportional to the […]

The Classification of Noisy Sequences Generated by Similar HMMs (Pattern Recognition and Machine Learning)

Abstract The method for classification performance improvement using hidden Markov models (HMM) is proposed. The k-nearest neighbors (kNN) classifier is used in the feature space produced by these HMM. Only the similar models with the noisy original sequences assumption are discussed. The research results on simulated data for two-class classification problem are presented. Keywords: Hidden […]

N DoT: Nearest Neighbor Distance Based Outlier Detection Technique (Pattern Recognition and Machine Learning)

Abstract In this paper, we propose a nearest neighbor based outlier detection algorithm, NDoT. We introduce a parameter termed as Nearest Neighbor Factor (NNF) to measure the degree of outlierness of a point with respect to its neighborhood. Unlike the previous outlier detection methods NDoT works by a voting mechanism. Voting mechanism binarizes the decision […]

Some Remarks on the Relation between Annotated Ordered Sets and Pattern Structures (Pattern Recognition and Machine Learning)

Abstract We exhibit an intimate connection between the concept of an annotated, ordered set and that of a pattern structure. This enables an exchange of ideas and techniques between both domains. Introduction Pattern structures were introduced in [KG01] to model information. The usefulness of annotated ordered sets for similar purposes was studied in [KSJ08]. Here, […]

Solving the Structure-Property Problem Using k-NN Classification (Pattern Recognition and Machine Learning)

Abstract The solution of the "structure-property" based on the molecular graphs descriptors selection with k-NN classifier is proposed. The results of comparing the construction of predictive models using the search and without it are given. The stability of the classifier function construction quality is tested using the test sample. Keywords: Pattern Recognition, QSAR, QSPR, k-NN. […]

Stable Feature Extraction with the Help of Stochastic Information Measure (Pattern Recognition and Machine Learning)

Abstract This article discusses the problem of extraction of such set of pattern features that is informative and stable with respect of stochastic noise. This is done through the stochastic information measure. Introduction In describing of non deterministic system we must take into account the nature of its uncertainty. In pattern recognition uncertainty could have […]

Wavelet-Based Clustering of Social-Network Users Using Temporal and Activity Profiles (Pattern Recognition and Machine Learning)

Abstract Encouraged by the success of social networking platforms, more and more enterprises are exploring the use of crowd-sourcing as a method for intra-organization knowledge management. There is not much information about their effectiveness though. While there has been some emphasis on studying friend networks, not much emphasis has been given towards understanding other kinds […]

Tight Combinatorial Generalization Bounds for Threshold Conjunction Rules (Pattern Recognition and Machine Learning)

Abstract We propose a combinatorial technique for obtaining tight data dependent generalization bounds based on a splitting and connectivity graph (SC-graph) of the set of classifiers. We apply this approach to a parametric set of conjunctive rules and propose an algorithm for effective SC-bound computation. Experiments on 6 data sets from the UCI ML Repository […]

An Improvement of Dissimilarity-Based Classifications Using SIFT Algorithm (Pattern Recognition and Machine Learning)

Abstract In dissimilarity-based classifications (DBCs), classifiers are not based on the feature measurements of individual objects, but rather on a suitable dissimilarity measure among the objects. In this paper, we study a new way of measuring the dissimilarity between two object images using a SIFT (Scale Invariant Feature Transformation) algorithm [5], which transforms image data […]

Introduction, Elimination Rules for — and D: A Study from Graded Context (Pattern Recognition and Machine Learning)

Abstract This paper is aimed to study the algebraic background of some proof theoretic rules in a set up where distinct levels of logic activity have been maintained carefully. In this regard, Introduction, Elimination rules for -i, and D have been considered as specific cases whose necessary and sufficient conditions from the perspective of graded […]