izvorni znanstveni rad
This paper summarizes a doctoral thesis introducing the new iterative approach to explainable feature learning. Features are learned in three steps during each iteration: feature construction, evaluation, and selection. We demonstrated superior performances compared to the state of the art on 13 of 15 test cases and the explainability of the learned feature representation for knowledge discovery.
data classification, explainable artificial intelligence, feature learning
Razvojem virtualizacijskih tehnologija raste kompleksnost IT sustava, dok korisnici istovremeno zahtijevaju brži pristup resursima, napredne integracije i jednostavnost korištenja. Jedno od ključnih rješenja za ove izazove primjena je…
Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…