Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: R-project

Sortuj według:

Ogranicz wyniki do:

A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario

Suchacka G., Skolimowska-Kulig M., Potempa A.

Journal of Telecommunications and Information Technology

2015

nr 3

64--69

This paper addresses the problem of classification of user sessions in an online store into two classes: buying sessions (during which a purchase confirmation occurs) and browsing sessions. As interactions connected with a purchase confirmation are typically completed at the end of user sessions, some information describing active sessions may be observed and used to assess the probability of making a purchase. The authors formulate the problem of predicting buying sessions in a Web store as a supervised classification problem where there are two target classes, connected with the fact of finalizing a purchase transaction in session or not, and a feature vector containing some variables describing user sessions. The presented approach uses the k-Nearest Neighbors (k-NN) classification. Based on historical data obtained from online bookstore log files a k-NN classifier was built and its efficiency was verified for different neighborhood sizes. A 11-NN classifier was the most effective both in terms of buying session predictions and overall predictions, achieving sensitivity of 87.5% and accuracy of 99.85%.

Szacowanie wielkości emisji CH4 produkcji zwierząt gospodarskich w Polsce z wykorzystaniem sieci neuronowej

Kolasa-Więcek A.

Postępy Techniki Przetwórstwa Spożywczego

2011

nr 1

117-120

Celem podjętych i przeprowadzonych badań była predykacja wielkości emisji CH4 z sektora rolniczego w Polsce na podstawie wielkości produkcji zwierząt gospodarskich. Prognozy prowadzono z wykorzystaniem sieci Flexible Bayesian Models. Poziom istotności analizowanych parametrów rozpatrzono w oparciu o testy Kendalla i Spearmana.

The aim of the study was the prediction of CH4 emissions from the agricultural sector in Poland on the basis of livestock production. Projections were carried out using the Bayesian Flexible Models network. The level of significance of the analyzed parameters were considered based on the Kendall and Spearman tests.