Výsledky vyhledávání - "Kati Iltanen"

Autor: Heidi Kari, Jaana Kekäläinen, Johanna Vainio, Kati Iltanen, Paavo Arvola, Marko Junkkari

Publikováno v: Journal of Database Management. 27:1-22

This article focuses on testing a path-oriented querying approach to hierarchical data in relational databases. The authors execute a user study to compare the path-oriented approach and traditional SQL from two perspectives: correctness of queries a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::87c22ffe62821d9460d52cbe170b92da
https://doi.org/10.4018/jdm.2016070101

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Machine learning method for knowledge discovery experimented with otoneurological data

Autor: Kirsi Varpa, Kati Iltanen, Martti Juhola

Publikováno v: Computer Methods and Programs in Biomedicine. 91:154-164

We have been interested in developing an otoneurological decision support system that supports diagnostics of vertigo diseases. In this study, we concentrate on testing its inference mechanism and knowledge discovery method. Knowledge is presented as

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c96a48e7d609bdb26a9c2612d86cad1d
https://doi.org/10.1016/j.cmpb.2008.03.003

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On computation of calcium cycling anomalies in cardiomyocytes data

Autor: Katriina Aalto-Setälä, Kirsi Varpa, Kirsi Penttinen, Harri Siirtola, Kati Iltanen, Martti Juhola, Henry Joutsijoki, Jyrki Rasku, Jorma Laurikkala, Jorge Avalos-Salguero, Jyri Saarikoski, Heikki Hyyrö

Publikováno v: EMBC

Induced pluripotent stem cell (iPSC) lines derived from skin fibroblasts of patients suffering from cardiac disorders were differentiated to cardiomyocytes and used to generate a data set of Ca(2+) transients of 136 recordings. The objective was to s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f6e7bf53ac9a4dcfba66c5ed622ccba
https://pubmed.ncbi.nlm.nih.gov/25570240

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Classification of iPSC colony images using hierarchical strategies with support vector machines

Autor: Jari Hyttinen, Kirsi Penttinen, Kirsi Varpa, Jyrki Rasku, Markus Haponen, Henry Joutsijoki, Michelangelo Paci, Jorma Laurikkala, Ivan Baldin, Jorge Avalos-Salguero, Yulia Gizatdinova, Kati Iltanen, Jyri Saarikoski, Katriina Aalto-Setälä, Martti Juhola, Harri Siirtola

Publikováno v: CIDM

In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::53e0e0dd368c2ded2337429593bf4b0e
https://doi.org/10.1109/cidm.2014.7008152

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Attribute weighting with Scatter and instance-based learning methods evaluated with otoneurological data

Autor: Markku Siermala, Kirsi Varpa, Martti Juhola, Kati Iltanen

Publikováno v: International Journal of Data Science. 2:173

Treating all attributes as equally important during classification can have a negative effect on the classification results. An attribute weighting is needed to grade the relevancy and usefulness of the attributes. Machine learning methods were utili

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d24322daa0303f2fe52e3539bb629476
https://doi.org/10.1504/ijds.2017.086257

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Genetic algorithm based approach in attribute weighting for a medical data set

Autor: Martti Juhola, Kirsi Varpa, Kati Iltanen

Genetic algorithms have been utilized in many complex optimization and simulation tasks because of their powerful search method. In this research we studied whether the classification performance of the attribute weighted methods based on the nearest

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1182cf79262adcc8192d3a9211d31a2
https://trepo.tuni.fi/handle/10024/99695

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Machine learning approach to an otoneurological classification problem

Autor: Henry Joutsijoki, Martti Juhola, Kirsi Varpa, Kati Iltanen

Publikováno v: EMBC

In this paper we applied altogether 13 classification methods to otoneurological disease classification. The main point was to use Half-Against-Half (HAH) architecture in classification. HAH structure was used with Support Vector Machines (SVMs), k-N

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64d88f1cf51a09ac3238ecd9b4b752b5
https://pubmed.ncbi.nlm.nih.gov/24109932

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Applying one-vs-one and one-vs-all classifiers in k-nearest neighbour method and support vector machines to an otoneurological multi-class problem

Autor: Kirsi, Varpa, Henry, Joutsijoki, Kati, Iltanen, Martti, Juhola

Publikováno v: Studies in health technology and informatics. 169

We studied how the splitting of a multi-class classification problem into multiple binary classification tasks, like One-vs-One (OVO) and One-vs-All (OVA), affects the predictive accuracy of disease classes. Classifiers were tested with an otoneurolo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::240e7daa24d9025ef54e865c4280143a
https://pubmed.ncbi.nlm.nih.gov/21893815

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