Zobrazeno 1 - 10
of 14
pro vyhledávání: '"K. Darshana Abeyrathna"'
Publikováno v:
IEEE Access, Vol 9, Pp 8233-8248 (2021)
Building models that are both interpretable and accurate is an unresolved challenge for many pattern recognition problems. In general, rule-based and linear models lack accuracy, while deep learning interpretability is based on rough approximations o
Externí odkaz:
https://doaj.org/article/4dc5e630da204f389583198cd9f0da75
Publikováno v:
Smart Innovation, Systems and Technologies ISBN: 9789811623233
Challenged by the effects of the COVID-19 pandemic, public transport is suffering from low ridership and staggering economic losses. One of the factors which triggered such losses was the lack of preparedness among governments and public transport pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c214880adcccef5f0f8958ae188ee0c5
https://doi.org/10.1007/978-981-16-2324-0_4
https://doi.org/10.1007/978-981-16-2324-0_4
Autor:
Vladimir A. Oleshchuk, Sasanka N. Ranasinghe, Harsha S. Gardiyawasam Pussewalage, K. Darshana Abeyrathna, Ole-Christoffer Granmo
Publikováno v:
SSCI
The rapid deployment in information and communication technologies and internet-based services have made anomaly based network intrusion detection ever so important for safeguarding systems from novel attack vectors. To this date, various machine lea
Publikováno v:
SSCI
The Tsetlin Machine (TM) is a recent interpretable machine learning algorithm that requires relatively modest computational power, yet attains competitive accuracy in several benchmarks. TMs are inherently binary; however, many machine learning probl
Publikováno v:
SSCI
Tsetlin machines (TMs) are a promising approach to machine learning that uses Tsetlin Automata to produce patterns in propositional logic, leading to binary (hard) classifications. In many applications, however, one needs to know the confidence of cl
Publikováno v:
IEEE Access, Vol 9, Pp 8233-8248 (2021)
IEEE Access
IEEE Access
Despite significant effort, building models that are both interpretable and accurate is an unresolved challenge for many pattern recognition problems. In general, rule-based and linear models lack accuracy, while deep learning interpretability is bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40f1eaf5de0f7287887938ac34828e0e
http://arxiv.org/abs/2005.05131
http://arxiv.org/abs/2005.05131
Autor:
Ole-Christoffer Granmo, Adrian Wheeldon, Alex Yakovlev, Morten Goodwin, Jie Lei, Rishad Shafik, K. Darshana Abeyrathna
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030637989
SGAI Conf.
SGAI Conf.
Due to the high energy consumption and scalability challenges of deep learning, there is a critical need to shift research focus towards dealing with energy consumption constraints. Tsetlin Machines (TMs) are a recent approach to machine learning tha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4246183d7d8e678515bdf60b0d601a6
https://doi.org/10.1007/978-3-030-63799-6_8
https://doi.org/10.1007/978-3-030-63799-6_8
Publikováno v:
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0aafcd8a837a67d351c9de81c7959956
https://hdl.handle.net/11250/2651754
https://hdl.handle.net/11250/2651754
Publikováno v:
Progress in Artificial Intelligence ISBN: 9783030302436
EPIA (2)
EPIA (2)
The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive clauses in propositional logic. In this paper, we go beyond pattern cla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2da380c9584d809969b9873176512ca3
https://doi.org/10.1007/978-3-030-30244-3_23
https://doi.org/10.1007/978-3-030-30244-3_23