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Anton Akusok
Researcher at Arcada University of Applied Sciences
Publications - 57
Citations - 1429
Anton Akusok is an academic researcher from Arcada University of Applied Sciences. The author has contributed to research in topics: Extreme learning machine & Random forest. The author has an hindex of 12, co-authored 56 publications receiving 1311 citations. Previous affiliations of Anton Akusok include Hanken School of Economics & Aalto University.
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Extreme Learning Machine
Erik Cambria,Guang-Bin Huang,Liyanaarachchi Lekamalage Chamara Kasun,Hongming Zhou,Chi-Man Vong,Jiarun Lin,Jianping Yin,Zhiping Cai,Qiang Liu,Kuan Li,Victor C. M. Leung,Liang Feng,Yew-Soon Ong,Meng-Hiot Lim,Anton Akusok,Amaury Lendasse,Francesco Corona,Rui Nian,Yoan Miche,Paolo Gastaldo,Rodolfo Zunino,Sergio Decherchi,Xuefeng Yang,Kezhi Mao,Beom-Seok Oh,Jehyoung Jeon,Kar-Ann Toh,Andrew Beng Jin Teoh,Jaihie Kim,Hanchao Yu,Yiqiang Chen,Junfa Liu +31 more
TL;DR: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation.
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High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications
TL;DR: This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELM) Toolbox for Big Data, and summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements.
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Extreme learning machine for missing data using multiple imputations
TL;DR: A novel methodology based on Gaussian Mixture Model and Extreme Learning Machine is developed to provide reliable estimates for the regression function (approximation), and final estimation is improved over the mean imputation performed only once to complete the data.
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Adaptive and online network intrusion detection system using clustering and Extreme Learning Machines
TL;DR: The main objective of this paper is to propose an adaptive design of intrusion detection systems on the basis of Extreme Learning Machines that offers the capability of detecting known and novel attacks and being updated according to new trends of data patterns provided by security experts in a cost-effective manner.
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A Two-Stage Methodology Using K-NN and False-Positive Minimizing ELM for Nominal Data Classification
TL;DR: Experimental results using a specific dataset provided by F-Secure Corporation show that this methodology provides a rapid decision on new samples, with a direct control over the false positives and thus on the decision capabilities of the model.