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1. Full Text A novel double deep ELMs ensemble system for time series forecasting
by Song, Gang and Dai, Qun
Knowledge-based systems, ISSN 0950-7051, 10/2017, Volume 134, pp. 31 - 49
Hierarchical ELM (H-ELM) | Time series forecasting (TSF) | Double deep ELMs ensemble system (DD-ELMs-ES) | Deep representations learning via ELM (dr-ELM) | Constrained H-ELM (CH-ELM) | Self-adaptive ReTSP-Trend (SA-ReTSP-Trend) | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology | Analysis | Forecasts and trends | Machine learning | Computer science | Algorithms | Neural networks | Time series | Mathematical models | Representations | Artificial intelligence | Forecasting
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2. Full Text Variances-constrained weighted extreme learning machine for imbalanced classification
by Liu, Zheng and Jin, Wei and Mu, Ying
Neurocomputing (Amsterdam), ISSN 0925-2312, 08/2020, Volume 403, pp. 45 - 52
Imbalanced data | VW-ELM | CCR-ELM | ELM | W-ELM | Control systems | Algorithms | Measuring instruments
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3. Full Text A semi-supervised online sequential extreme learning machine method
by Jia, Xibin and Wang, Runyuan and Liu, Junfa and Powers, David M.W
Neurocomputing (Amsterdam), ISSN 0925-2312, 01/2016, Volume 174, pp. 168 - 178
Semi-supervised ELM (SS-ELM) | Semi-supervised online sequential ELM (SOS-ELM) | Online Sequential ELM (OS-ELM) | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology | Algorithms | Data mining | Analysis | Methods
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4. Full Text Optimization method based extreme learning machine for classification
by Huang, Guang-Bin and Ding, Xiaojian and Zhou, Hongming
Neurocomputing (Amsterdam), ISSN 0925-2312, 2010, Volume 74, Issue 1, pp. 155 - 163
Support vector network | Minimal norm of weights | Extreme learning machine | Support vector machine | ELM feature space | ELM kernel | Equivalence between ELM and SVM | Maximal margin | Primal and dual ELM networks | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology | Learning | Support vector machines | Computer simulation | Neural networks | Mathematical analysis | Classification | Standards | Optimization
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5. Full Text Extreme Learning Machines [Trends & Controversies]
by Cambria, Erik and Huang, Guang-Bin and Kasun, Liyanaarachchi Lekamalage Chamara and Zhou, Hongming and Vong, Chi Man and Lin, Jiarun and Yin, Jianping and Cai, Zhiping and Liu, Qiang and Li, Kuan and Leung, Victor C.M and Feng, Liang and Ong, Yew-Soon and Lim, Meng-Hiot and Akusok, Anton and Lendasse, Amaury and Corona, Francesco and Nian, Rui and Miche, Yoan and Gastaldo, Paolo and Zunino, Rodolfo and Decherchi, Sergio and Yang, Xuefeng and Mao, Kezhi and Oh, Beom-Seok and Jeon, Jehyoung and Toh, Kar-Ann and Teoh, Andrew Beng Jin and Kim, Jaihie and Yu, Hanchao and Chen, Yiqiang and Liu, Junfa
IEEE intelligent systems, ISSN 1541-1672, 11/2013, Volume 28, Issue 6, pp. 30 - 59
signature biometrics | deep networks | visualization | random projections | ELM | computation outsourcing | Nonhomogeneous media | ensemble | Learning systems | partitioned ELM | Kinect | evolutionary optimization | random permutations | representational learning | Big data | text mining | cloud computing | Adaptive learning | hand gesture signature verification | Special issues and sections | memetic computation | meta meme | Gesture recognition | OS-ELM | Biological neural networks | human-computer interaction | Data visualization | extreme learning machine | knowledge extraction | Machine learning | Artificial intelligence | online sequential ELM | Engineering, Electrical & Electronic | Engineering | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology | Applied sciences | Connectionism. Neural networks | Exact sciences and technology | Computer science; control theory; systems | Learning | Permutations | Cloud computing | Neural networks | Projection | Outsourcing | Signatures | Recognition
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6. Full Text Clustering in extreme learning machine feature space
by He, Qing and Jin, Xin and Du, Changying and Zhuang, Fuzhen and Shi, Zhongzhi
Neurocomputing (Amsterdam), ISSN 0925-2312, 03/2014, Volume 128, pp. 88 - 95
Extreme learning machine (ELM) | ELM feature space | ELM kMeans | Nonnegative matrix factorization (NMF) | ELM NMF clustering | Data clustering | Memory organisation. Data processing | Exact sciences and technology | Algorithmics. Computability. Computer arithmetics | Connectionism. Neural networks | Mathematics | Sciences and techniques of general use | Computer science; control theory; systems | Data processing. List processing. Character string processing | Algebra | Linear and multilinear algebra, matrix theory | Theoretical computing | Applied sciences | Software | Artificial intelligence | Algorithms | Learning | Approximation | Mathematical analysis | Elm | Mapping | Clustering | Factorization | Neural networks
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7. Full Text First observations of ELM triggering by injected lithium granules in EAST
by Mansfield, D.K and Roquemore, A.L and Carroll, T and Sun, Z and Hu, J.S and Zhang, L and Liang, Y.F and Gong, X.Z and Li, J.G and Guo, H.Y and Zuo, G.Z and Parks, P and Wu, W and Maingi, R
Nuclear fusion, ISSN 0029-5515, 11/2013, Volume 53, Issue 11, pp. 113023 - 7
ELM Mitigation | Edge-Localized Modes | Lithium | ELM Pacing | ELM Triggering | Pellet injection | Physical Sciences | Physics, Fluids & Plasmas | Physics | Science & Technology | Decoupling | Granular materials | Elm | Granules | Devices | Ablation | Pellets
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8. Full Text Progress on the application of ELM control schemes to ITER scenarios from the non-active phase to DT operation
by Loarte, A and Huijsmans, G and Futatani, S and Baylor, L.R and Evans, T.E and Orlov, D. M and Schmitz, O and Becoulet, M and Cahyna, P and Gribov, Y and Kavin, A and Sashala Naik, A and Campbell, D.J and Casper, T and Daly, E and Frerichs, H and Kischner, A and Laengner, R and Lisgo, S and Pitts, R.A and Saibene, G and Wingen, A
Nuclear fusion, ISSN 0029-5515, 2/2014, Volume 54, Issue 3, pp. 33007 - 18
pellet pacing | ITER | ELM (edge localized mode) | ELM control | RMP (resonant magnetic perturbation) | Physical Sciences | Physics, Fluids & Plasmas | Physics | Science & Technology | Coils | Nuclear fusion | Stability | Elm | Control systems | Feasibility | Plasma currents | Models
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9. Full Text On the construction of extreme learning machine for online and offline one-class classification—An expanded toolbox
by Gautam, Chandan and Tiwari, Aruna and Leng, Qian
Neurocomputing (Amsterdam), ISSN 0925-2312, 10/2017, Volume 261, pp. 126 - 143
Extreme learning machine (ELM) | Online sequential ELM (OSELM) | One-class classification (OCC) | One-class ELM (OCELM) | Autoassociative ELM (AAELM)
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10. Full Text Grassy-ELM regime with edge resonant magnetic perturbations in fully noninductive plasmas in the DIII-D tokamakaaEdge-localized-mode
by Nazikian, R and Petty, C.C and Bortolon, A and Chen, Xi and Eldon, D and Evans, T.E and Grierson, B.A and Ferraro, N.M and Haskey, S.R and Knolker, M and Lasnier, C and Logan, N.C and Moyer, R.A and Orlov, D and Osborne, T.H and Paz-Soldan, C and Turco, F and Wang, H.Q and Weisberg, D.B
Nuclear fusion, ISSN 0029-5515, 10/2018, Volume 58, Issue 10
pedestal | RMP | ELM
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11. Full Text Effect of resonant magnetic perturbations on low collisionality discharges in MAST and a comparison with ASDEX Upgrade
by Kirk, A and Suttrop, W and Chapman, I.T and Liu, Yueqiang and Scannell, R and Thornton, A.J and Orte, L. Barrera and Cahyna, P and Eich, T and Fischer, R and Fuchs, C and Ham, C and Harrison, J.R and Jakubowski, M.W and Kurzan, B and Pamela, S and Peterka, M and Ryan, D and Saarelma, S and Sieglin, B and Valovic, M and Willensdorfer, M and ASDEX Upgrade Team and MAST Upgrade Team and ASDEX Upgrade Teams and MAST
Nuclear fusion, ISSN 0029-5515, 3/2015, Volume 55, Issue 4, pp. 43011 - 15
ELM energy loss | ELM mitigation | AUG | MAST | Physical Sciences | Physics, Fluids & Plasmas | Physics | Science & Technology | Masts | Perturbation methods | Mathematical analysis | Elm | Distortion | Boundaries | Density | Internal energy | Physics - Plasma Physics
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12. Full Text An incremental extreme learning machine for online sequential learning problems
by Guo, Lu and Hao, Jing-hua and Liu, Min
Neurocomputing (Amsterdam), ISSN 0925-2312, 03/2014, Volume 128, pp. 50 - 58
Extreme learning machine (ELM) | Fixed size LSSVM (FS-LSSVM) | Online sequential ELM (OS-ELM) | Incremental learning algorithm | Incremental ELM (IELM) | Memory organisation. Data processing | Exact sciences and technology | Microelectronic fabrication (materials and surfaces technology) | Electronics | Algorithmics. Computability. Computer arithmetics | Connectionism. Neural networks | Computer science; control theory; systems | Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices | Data processing. List processing. Character string processing | Theoretical computing | Applied sciences | Software | Artificial intelligence | Data mining | Analysis | Algorithms | Learning | Demand | Semiconductors | Neural networks | Mathematical models | Retraining | Online
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13. Full Text Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors
by Heddam, Salim and Kisi, Ozgur
Environmental science and pollution research international, ISSN 0944-1344, 7/2017, Volume 24, Issue 20, pp. 16702 - 16724
Environmental Health | Ecotoxicology | Extreme learning machine | OS-ELM | S-ELM | Modeling | Environment, general | OP-ELM | MLPNN | R-ELM | MLR | Environment | Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution | Dissolved oxygen | Environmental Chemistry | Atmospheric Protection/Air Quality Control/Air Pollution | Environmental Sciences & Ecology | Life Sciences & Biomedicine | Science & Technology | Environmental Sciences | Linear Models | Oxygen | Rivers | Neural Networks (Computer) | Water Quality | Distance learning | Ecosystems | Activation | Datasets | Training | Learning | Accuracy | Geological surveys | Temperature effects | Basins | Turbidity | Mathematical models | Aversion learning | Statistical analysis | Ponds | Data processing | Regression analysis | Water temperature | Resistance | Stations | Learning algorithms | Serial learning | Neural networks | Water quality | Environment models | Models | Conductance | Artificial intelligence | Index Medicus
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