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With the current flood of information and services on the Web it is necessary to have models of information processing which provide ordering of results by relevance tailored to each user/customer separately.
The aim of the lecture is to inter-link several information models (mainly formal declarative / deductive and inductive) and extend them with ordering.
Solution is demonstrated by LMPM – Linear Monotone Preference Model. In labs we work with some formal models (preferential Datalog), graphical version of LMPM and experiments on small illustrative data.
Last update: Kopecký Michal, RNDr., Ph.D. (09.05.2019)
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Following conditions are "corona"-dependent subject to change, actual form of deliverables and timetable will be settled and announced on classroom page for definitely enrolled students when "corona"-conditions change.
Terms of passing the course consist of homework, mainly on paper and some about experimenting with small illustrative data. These are only conditions for getting credits. Exam is oral and requires basic understanding of the whole material. As soon as terminology is introduced, detailed milestones (also form of deliverables) and preferred deadlines (with possible repeated attempts) will be announced at a lab. There is no evidence on personal presence. Nevertheless, no additional explanation for tasks will be given, except on the respective lab and brief description on the course web. Final deadline is end of semester. Last update: Vojtáš Peter, prof. RNDr., DrSc. (27.10.2020)
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Last update: Kopecký Michal, RNDr., Ph.D. (09.05.2019)
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Information models and ordering
Motivation problems, use-case, data, challenge, goal, who/what is better, ordering as preference Various representation and presentations of ordering in data, information, knowledge Linear Monotone Preference Model Fagin's data model and threshold top-k algorithm - Deduction-Querying, Search, Retrieval, ...Linear Monotone Preference Model a Fagin’s data model Threshold algorithm, Correctness Measures of success of preference learning algorithms - Induction-Learning, Generalization, Estimation, PredictionClass of models System of metrics Experiments, validation, evaluation Datalog / logic programming - logical/relational domain calculus with orderingMany valued characteristics of sets as a tool for coding ordering, many valued logic, connectives Many valued modus ponens - declarative models - model of deduction, induction, querying Many valued modus ponens, residuated operators and correctness Many valued logic programming and correctness Last update: Vojtáš Peter, prof. RNDr., DrSc. (23.05.2020)
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