| | SLO | ENG | Piškotki in zasebnost

Večja pisava | Manjša pisava

Iskanje po katalogu digitalne knjižnice Pomoč

Iskalni niz: išči po
išči po
išči po
išči po
* po starem in bolonjskem študiju

Opcije:
  Ponastavi


1 - 6 / 6
Na začetekNa prejšnjo stran1Na naslednjo stranNa konec
1.
2.
Statistically significant features improve binary and multiple motor imagery task predictions from EEGs
Murside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Yalcin Isler, 2023, izvirni znanstveni članek

Opis: In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require.
Ključne besede: brain-computer interfaces, electroencephalogram, feature selection, machine learning, task classification
Objavljeno v DKUM: 10.09.2024; Ogledov: 31; Prenosov: 8
.pdf Celotno besedilo (1,15 MB)
Gradivo ima več datotek! Več...

3.
4.
DynFS: dynamic genotype cutting feature selection algorithm
Dušan Fister, Iztok Fister, Sašo Karakatič, 2023, izvirni znanstveni članek

Ključne besede: feature selection, nature-inspired algorithms, swarm intelligence, optimization
Objavljeno v DKUM: 05.04.2024; Ogledov: 218; Prenosov: 22
.pdf Celotno besedilo (1,14 MB)

5.
6.
Classification of white varietal wines using chemical analysis and sensorial evaluations
Katja Šnuderl, Jan Mocak, Darinka Brodnjak-Vončina, Bibiana Sedláčkova, 2009, izvirni znanstveni članek

Opis: The ways of application of multivariate data analysis and ANOVA to classification of white varietal wines are here demonstrated. Wine classification was performed using the following classification criteria: winevariety, year of production, wine producer, and wine quality, as found by sensorial testing (bouquet, colour, and taste). Subjective wine evaluation, made by wine experts, is combined with commonly used chemical and physico-chemical properties, measured in analytical laboratory. Importance of the measured variables was determined by principal component analysis and confirmed by analysis of variance. Linear discriminant analysis enabled not only a very successful wine classification but also prediction of the wine category for unknown samples. The wine categories were set up either by three wine varieties, or two vintages, wine producers; two or three wine categories established by wine quality reflected either total points obtained in sensorial evaluation or the points obtained for a particular quality descriptor like colour, taste and bouquet.
Ključne besede: multivariate data analysis, principal component analysis, discriminant analysis, feature selection, ANOVA, sensory analysis
Objavljeno v DKUM: 31.05.2012; Ogledov: 2308; Prenosov: 112
.pdf Celotno besedilo (209,77 KB)
Gradivo ima več datotek! Več...

Iskanje izvedeno v 0.05 sek.
Na vrh
Logotipi partnerjev Univerza v Mariboru Univerza v Ljubljani Univerza na Primorskem Univerza v Novi Gorici