Autor: Kordos, Mirosław - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Effectiveness of Unsupervised Training in Deep Learning Neural Networks
Autorzy:: Rusiecki, Andrzej
Kordos, Mirosław
Powiązania:: https://bibliotekanauki.pl/articles/1373690.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
Tematy:: neural networks
deep learning
restricted Boltzmann Machine
contrastive divergence
Opis:: Deep learning is a field of research attracting nowadays much attention, mainly because deep architectures help in obtaining outstanding results on many vision, speech and natural language processing – related tasks. To make deep learning effective, very often an unsupervised pretraining phase is applied. In this article, we present experimental study evaluating usefulness of such approach, testing on several benchmarks and different percentages of labeled data, how Contrastive Divergence (CD), one of the most popular pretraining methods, influences network generalization.
Źródło:: Schedae Informaticae; 2015, 24; 41-51
0860-0295
2083-8476
Pojawia się w:: Schedae Informaticae
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Fast image index for database management engines
Autorzy:: Grycuk, Rafał
Najgebauer, Patryk
Kordos, Miroslaw
Scherer, Magdalena M.
Marchlewska, Alina
Powiązania:: https://bibliotekanauki.pl/articles/1837480.pdf
Data publikacji:: 2020
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: image descriptor
content-based image retrieval
image indexing
Opis:: Large-scale image repositories are challenging to perform queries based on the content of the images. The paper proposes a novel, nested-dictionary data structure for indexing image local features. The method transforms image local feature vectors into two-level hashes and builds an index of the content of the images in the database. The algorithm can be used in database management systems. We implemented it with an example image descriptor and deployed in a relational database. We performed the experiments on two image large benchmark datasets.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 113-123
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Efficient image retrieval by fuzzy rules from boosting and metaheuristic
Autorzy:: Korytkowski, Marcin
Senkerik, Roman
Scherer, Magdalena M.
Angryk, Rafal A.
Kordos, Miroslaw
Siwocha, Agnieszka
Powiązania:: https://bibliotekanauki.pl/articles/91856.pdf
Data publikacji:: 2020
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: image retrieval
fuzzy rules
local image features
pobieranie obrazu
lokalne funkcje obrazu
Opis:: Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 57-69
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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