Repozitorij Univerze v Novi Gorici

Iskanje po repozitoriju
A+ | A- | Pomoč | SLO | ENG

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

Opcije:
  Ponastavi


1 - 5 / 5
Na začetekNa prejšnjo stran1Na naslednjo stranNa konec
1.
A multi-criteria decision-making model for classifying wood products with respect to their impact on environment
Igor Lipušček, Marko Bohanec, Leon Oblak, Lidija Zadnik Stirn, 2010, izvirni znanstveni članek

Najdeno v: ključnih besedah
Povzetek najdenega: ...wood manufacturing products, classification, multi-criteria decision, support model, ...
Ključne besede: wood manufacturing products, classification, multi-criteria decision, support model
Objavljeno: 15.10.2013; Ogledov: 4547; Prenosov: 107
URL Polno besedilo (0,00 KB)

2.
GRB 160227A: NOT redshift
Tanja Petrushevska, Dong Xu, 2016, končno poročilo o rezultatih raziskav

Najdeno v: ključnih besedah
Povzetek najdenega: ...Gamma Ray Burst classification, GRB, Nordic Optic Telescope...
Ključne besede: Gamma Ray Burst classification, GRB, Nordic Optic Telescope
Objavljeno: 24.01.2018; Ogledov: 2250; Prenosov: 0
.pdf Polno besedilo (49,11 KB)

3.
4.
Exploring deep learning as an event classification method for the Cherenkov Telescope Array
Marko Zavrtanik, Danilo Zavrtanik, Gabrijela Zaharijas, Lili Yang, Serguei Vorobiov, Samo Stanič, Gašper Kukec Mezek, Christopher Eckner, D. Nieto, 2017, objavljeni znanstveni prispevek na konferenci

Najdeno v: ključnih besedah
Ključne besede: CTA, event classification, deep learning
Objavljeno: 16.02.2018; Ogledov: 2254; Prenosov: 114
.pdf Polno besedilo (313,07 KB)

5.
Click-through rate estimation using CHAID classification tree model
Rajan Gupta, Saibal K. Pal, 2019, objavljeni znanstveni prispevek na konferenci

Opis: Click-Through Rate (CTR) is referred to as the number of clicks on a particular advertisement as compared to the number of impressions on it. It is an important measure to find the effectiveness of any online advertising campaign. The effectiveness of online advertisements through calculations of ROI can be done through the measurement of CTR. There are multiple ways of detecting CTR in past; however, this study focuses on machine learning based classification model. Important parameters are judged on the basis of user behavior toward online ads and CHAID tree model is used to classify the pattern for successful and unsuccessful clicks. The model is implemented using SPSS version 21.0. The dataset used for the testing has been taken from Kaggle website as the data is from anonymous company’s ad campaign given to Kaggle for research purpose. A total of 83.8% accuracy is reported for the classification model used. This implies that CHAID can be used for less critical problems where very high stakes are not involved. This study is useful for online marketers and analytics professionals for assessing the CHAID model’s performance in online advertising world.
Najdeno v: ključnih besedah
Ključne besede: click-through rate, online advertisements, classification tree, mobile ads, click estimation
Objavljeno: 02.04.2021; Ogledov: 654; Prenosov: 3
URL Polno besedilo (0,00 KB)
Gradivo ima več datotek! Več...

Iskanje izvedeno v 0 sek.
Na vrh