Search Results - "Unsupervised Machine Learning Methods"

La creativitat en el futbol com a fenomen complex. Eines d’avaluació i propostes per desenvolupar-la

Authors: Canton, Albert

Thesis Advisors: Torrents Martín, Carlota, Hristovski, Robert, Institut Nacional d'Educació Física de Catalunya (Lleida)

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Complexitat, Jocs reduïts, Dynamic overlap, Anàlisi de components principals, Coeficient de congruència de Tucker, Esports d’equip, Unsupervised Machine Learning Methods, Complejidad, Juegos reducidos, Análisis de componentes principales, Coeficiente de congruencia de Tucker, Deportes de equipo, Complexity, Small-sided games, Principal components analysis, Tucker's congruence coefficient, Team sports, Educació Física i Esportiva

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/687552

Unsupervised induction of semantic roles

Authors: Lang, Joel Lapata, Mirella. Sutton, Charles

Contributors: Lang, Joel Lapata, Mirella. Sutton, Charles

Subject Terms: 006.35, automatic frame-semantic analysis., natural language, unsupervised machine learning methods, semantic role induction, semantic roles

Access URL: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.563735

La creativitat en el futbol com a fenomen complex. Eines d’avaluació i propostes per desenvolupar-la

Authors: Canton, Albert Torrents Martín, Carlota Hristovski, Robert et al.

Contributors: Canton, Albert Torrents Martín, Carlota Hristovski, Robert et al.

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Complexitat, Jocs reduïts, Dynamic overlap, Anàlisi de components principals, Coeficient de congruència de Tucker, Esports d’equip, Unsupervised Machine Learning Methods, Complejidad, Juegos reducidos, Análisis de componentes principales, Coeficiente de congruencia de Tucker, Deportes de equipo, Complexity, Small-sided games, Principal components analysis, Tucker's congruence coefficient, Team sports, Educació Física i Esportiva

Time: 79

Relation: https://hdl.handle.net/10803/687552; https://hdl.handle.net/10459.1/466166

Availability: https://hdl.handle.net/10803/687552
https://hdl.handle.net/10459.1/466166

Система прогнозування кредитного ризику на основі поєднання методів навчання з вчителем і без вчителя

Contributors: Недашківська, Надія Іванівна ELAKPI

Subject Terms: peer-to peer platform, оцінювання кредитного ризику, ансамблі, стратегія консенсу som, credit risk forecating, пірингові платформи, ensembles, integration of supervised and unsupervised machine learning methods, outlier detection, som consensus model, аналіз викидів в даних, поєднання навчання з вчителем та без вчителя

File Description: application/pdf

Access URL: https://ela.kpi.ua/handle/123456789/60429

A comparative analysis of unsupervised machine-learning methods in PSG-related phenotyping.

Authors: Ghorvei M Karhu T Hietakoste S et al.

Source: Journal of sleep research [J Sleep Res] 2025 Jun; Vol. 34 (3), pp. e14349. Date of Electronic Publication: 2024 Oct 24.

Publication Type: Journal Article; Comparative Study; Research Support, Non-U.S. Gov't

Journal Info: Publisher: Published on behalf of the European Sleep Research Society by Blackwell Scientific Publications Country of Publication: England NLM ID: 9214441 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-2869 (Electronic) Linking ISSN: 09621105 NLM ISO Abbreviation: J Sleep Res Subsets: MEDLINE

MeSH Terms: Sleep Apnea, Obstructive*/diagnosis , Sleep Apnea, Obstructive*/physiopathology , Polysomnography*/methods , Unsupervised Machine Learning*, Humans ; Cluster Analysis ; Phenotype ; Male ; Female ; Middle Aged ; Fuzzy Logic ; Adult