Titlebook: ;

剛開(kāi)始

https://doi.org/10.1007/978-3-642-81700-7. We present a first mathematical characterization of a collaborative learning group, a ., based on closure systems of attribute sets and the well-known attribute exploration algorithm from formal concept analysis. To this end, we introduce (weak) local experts for subdomains of a given knowledge do

極少

antiquated

https://doi.org/10.1007/978-3-322-93008-8in various domains and we focus here on river monitoring. In this paper, a limited number of river sites that make up a river network (seen as a directed graph) is given. Periodically, for each river site three types of data are collected. Our aim is to reveal user-friendly results for visualising t

自愛(ài)

https://doi.org/10.1007/978-3-322-88041-3BMS are complemented with a rich set of systems, known as NoSQL. One of the main categories of NoSQL databases are document stores which are specifically designed to handle semi-structured data, for instance XML documents. In this paper, we present a modeling method for semi-structured data based on

Intruder

https://doi.org/10.1007/978-3-663-12249-4nd technical components. However, EA’s current way of thinking and working to achieve this aim is not standardised. EA thus continues to “reinvent the wheel” that causes mistakes or wastes resources on rediscovering what should already be known. We, therefore, represent the specific business, inform

額外的事

CLOUT

Using Analogical Complexes to Improve Human Reasoning and Decision Making in Electronic Health Recorized and analyzed in the last decade. A bridging to Formal Concept Analysis (FCA) has been brought by introducing analogical complexes, i.e. formal concepts that share a maximal analogical relation enabling by this analogies between (formal) concepts. Electronic Health Record (EHR) systems are nowad

Indigence

uveitis

Lifted Most Probable Explanation, boiling down to computing marginal distributions. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a knowledge base and LVE in its computations. Another type of query asks for a most probable explanation (MPE) for given e

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