Arbitrary weight changes in dynamic trees
Arrow's Impossibility Theorem
A formalization of the first proof from [6].
Artificial intelligence methods in diagnostics of analog systems
Artificial neural networks. A review from physical and mathematical points of view
Artificial neural networks in time series forecasting: a comparative analysis
Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear time series...
Asymmetry level as a fuzzy measure.
Attempts at axiomatic description of conditional independence
Automated theorem proving in monadic predicate calculus
Automatic Identification of False Friends in Parallel Corpora: Statistical and Semantic Approach
False friends are pairs of words in two languages that are perceived as similar but have different meanings. We present an improved algorithm for acquiring false friends from sentence-level aligned parallel corpus based on statistical observations of words occurrences and co-occurrences in the parallel sentences. The results are compared with an entirely semantic measure for cross-lingual similarity between words based on using the Web as a corpus through analyzing the words’ local contexts extracted...
Automatic listing of important observational statements. I
Automatic listing of important observational statements. II
Automatic listing of important observational statements. III
Automatic speech signal segmentation based on the innovation adaptive filter
Automatic Video Annotation with Forests of Fuzzy Decision Trees.
Automatická syntaktická analýza českého textu (Experiment)
Automaty a mozek
Â?Wahrscheinlichkeitslernen" in der statistischen Lerntheorie.
Axiomatisation de la notion de repérage abstrait
Bayesian estimation of mixtures with dynamic transitions and known component parameters
Probabilistic mixtures provide flexible “universal” approximation of probability density functions. Their wide use is enabled by the availability of a range of efficient estimation algorithms. Among them, quasi-Bayesian estimation plays a prominent role as it runs “naturally” in one-pass mode. This is important in on-line applications and/or extensive databases. It even copes with dynamic nature of components forming the mixture. However, the quasi-Bayesian estimation relies on mixing via constant...
Belief functions induced by multimodal probability density functions, an application to the search and rescue problem
In this paper, we propose a new method to generate a continuous belief functions from a multimodal probability distribution function defined over a continuous domain. We generalize Smets' approach in the sense that focal elements of the resulting continuous belief function can be disjoint sets of the extended real space of dimension n. We then derive the continuous belief function from multimodal probability density functions using the least commitment principle. We illustrate the approach on two...