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Subject: search.filters.subject.machine learning

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    The Approach to Creating the Recommendation System of Piano Pieces
    (2021-05-04) Holshtein, Maiia; Babkova, Nadiia; National Technical University "Kharkiv Polytechnic Institute"
    Nowadays a lot of descriptions of pieces of musical art can be found in Internet or in specialized collections. There is no recommendation system that offers certain composition for performance according to its difficulty level. This paper suggests the approach to creating the recommendation system of piano pieces. The approach is based on checking for collocations in descriptions of each composition. This paper shows the statistical method PMI used for searching the collocations indicating on certain difficulty level. In addition it also discusses the main problems during creating own recommendation system.
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    A(n) Assumption in machine learning
    (Lviv Politechnic Publishing House, 2019-04-18) Klyushin, Dmitry; Lyashko, Sergey; Zub, Stanislav; Taras Shevchenko National University of Kyiv
    The commonly used statistical tools in machine learning are two-sample tests for verifying hypotheses on homogeneity, for example, for estimation of corpushomogeneity, testing text authorship and so on. Often, they are effective only for sufficiently large sample (n> 100) and have limited application in situations where the size of samples is small (n < 30). To solve the problem for small samples, methods of reproducing samples are often used: jackknife and bootstrap. We propose and investigate a family of homogeneity measures based on A(n) assumption that are effective both for small and large samples.
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    Handwritten characters recognition using modification of classic Rosenblatt’s perceptron extended with multiple S-layers
    (Lviv Polytechnic Publishing House, 2016) Serdyuk, Pavlo; Mosiiuk, Vasyl; Lviv Polytechnic National University
    Presented structure and principles of perceptron with multiple sensor layers. Demonstrated how it recognize character image and vote for result.
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    Neural network technology for image downscaling
    (Lviv Polytechnic Publishing House, 2015) Tytyk, Roman; Tkachenko, Roman; Izonin, Ivan; Hrytsyk, Kateryna; Lviv Polytechnic National University
    In this paper described for the first time neural network technology developed to zoom out images scale. The developed method uses a neural structure model geometric transformations to establish the connections between frames pairs of images of high and low resolution. Adjusted so in the learning process synaptic scales allow to change the scale of images of one class without substantial loss of quality.
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    A method of construction of automated basic ontology
    (National Technical University «KhPI», 2017) Lytvyn, Vasyl; Vysotska, Victoria; Wojcik, Waldemar; Dosyn, Dmytro; Lviv Polytechnic National University; Institute of Electronics and Information Technology, Lublin University of Technology; Systems Analysis Laboratory, Karpenko Physico-Mechanical Institute of the NAS of Ukraine
    The paper describes an approach to development of a computer system that automatically constructs an ontology base. Basic modules of the system and its operation are described, as well as the choice of software tools for implementation. Application of the proposed system allows to fill the domain ontology in an automatic mode. Therefore, this paper introduces an approach to development of an automated basic ontology composition. An architecture of synthesis of the ontology system is created using CROCUS (Cognition Relations or Concepts Using Semantics) software model. The main system modules and their functions are described. A decision of SDK for system realization is justified. Application of the proposed system can fill an ontology of subject area automatically.
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    Intelligent system structure for Web resources processing and analysis
    (National Technical University «KhPI», 2017) Lytvyn, Vasyl; Vysotska, Victoria; Chyrun, Lyubomyr; Smolarz, Andrzej; Naum, Oleh; Lviv Polytechnic National University; Institute of Electronics and Information Technology, Lublin University of Technology; Information Systems and Technologies Department, Drohobych Ivan Franko State Pedagogical University
    The paper describes the general detailed and formal description of intelligent system of information resources processing (ISIRP) based ontology. The content life cycle phase implementation of ISIRP structure is improved. The general principles of ISIRP designing structures enable automated information resource processing to increase regular user text content realization, reducing the production cycle, saving time and increasing the e-commerce capabilities.