Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Fazli Can"'
Publikováno v:
IEEE Access, Vol 11, Pp 89315-89330 (2023)
In a data stream environment, classification models must effectively and efficiently handle concept drift. Ensemble methods are widely used for this purpose; however, the ones available in the literature either use a large data chunk to update the mo
Externí odkaz:
https://doaj.org/article/5b8e04a5628543a8838f833fe1c2c6e5
Autor:
KÜÇÜK, DILEK1 dilek.kucuk@tubitak.gov.tr, FAZLI CAN2 canf@cs.bilkent.edu.tr
Publikováno v:
ACM Computing Surveys. Jan2021, Vol. 53 Issue 1, p1-37. 37p.
Autor:
FAZLI CAN, Ege Berkay Gülcan
Publikováno v:
Artificial Intelligence Review
Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases. Changes in data distribution, also known as concept drift, cause existing classification models to rapidly lose their
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3fc304856b9932401ab719c6eebd5b9
https://hdl.handle.net/11693/111506
https://hdl.handle.net/11693/111506
Publikováno v:
Journal of Quantitative Linguistics
We present the first quantitative analysis of spoken discourse for the Turkish language using memoirs of a group of old-time moviegoers of varying age groups whose birth year spreads over a period of four decades ranging from the 1930s to the 1960s.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::199bf3aa3f3702c2d19fd6f166429c69
https://aperta.ulakbim.gov.tr/record/238482
https://aperta.ulakbim.gov.tr/record/238482
Autor:
Dilek Küçük, Fazli Can
Conference Name: WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Date of Conference: February 21 - 25, 2022 Stance detection (also known as stance classification, stance prediction, and stance analysi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c700ff1dc330ef6737f969ba60fc2084
https://hdl.handle.net/11693/111714
https://hdl.handle.net/11693/111714
Autor:
Fazli Can, Dilek Küçük
Publikováno v:
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
SIGIR
SIGIR
Conference Name: SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval Date of Conference: 11-15 July 2021 Stance detection (also known as stance classification and stance predictio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::787ad57c7ad5553d6e98a1a2522e0e93
https://hdl.handle.net/11693/77159
https://hdl.handle.net/11693/77159
Autor:
Fazli Can, Ömer Gözüaçık
Publikováno v:
Artificial Intelligence Review
Data stream mining has become an important research area over the past decade due to the increasing amount of data available today. Sources from various domains generate a near-limitless volume of data in temporal order. Such data are referred to as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c8c24c6d34e0ad78fda89335ec9eb58
https://hdl.handle.net/11693/77042
https://hdl.handle.net/11693/77042
Autor:
Sevil Çalışkan, Fazli Can
Publikováno v:
Turk Kutuphaneciligi - Turkish Librarianship. 32:251-286
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d51501629e4972a638cea48c3e2eed00
https://doi.org/10.24146/tkd.2018.41
https://doi.org/10.24146/tkd.2018.41
Autor:
Dilek Küçük, Fazli Can
Publikováno v:
ACM Computing Surveys
Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d24589aab4859949efdc8151def183c
https://hdl.handle.net/11693/55034
https://hdl.handle.net/11693/55034
Autor:
Hamed Bonab, Fazli Can
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems
The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of existing batch and online ensemb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::064625a7d5300f084d6744bb6cab7528
https://pubmed.ncbi.nlm.nih.gov/30629518
https://pubmed.ncbi.nlm.nih.gov/30629518