-
1
-
-
85018991558
-
The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms
-
Makridakis S. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures. 2017; 90:46-60. https://doi.org/10.1016/j.futures.2017.03.006
-
(2017)
Futures
, vol.90
, pp. 46-60
-
-
Makridakis, S.1
-
2
-
-
0003123930
-
Forecasting with artificial neural networks:: The state of the art
-
Zhang G, Eddy Patuwo B, Hu Y M. Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting. 1998; 14(1):35-62. https://doi.org/10.1016/S0169-2070(97)00044-7
-
(1998)
International Journal of Forecasting
, vol.14
, Issue.1
, pp. 35-62
-
-
Zhang, G.1
Eddy Patuwo, B.2
Hu, Y.M.3
-
3
-
-
56349131204
-
Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting
-
Hamzacebi C, Akay D, Kutay F. Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications. 2009; 36(2, Part 2):3839-3844. https://doi.org/10.1016/j.eswa.2008.02.042.
-
(2009)
Expert Systems with Applications
, vol.36
, Issue.2
, pp. 3839-3844
-
-
Hamzacebi, C.1
Akay, D.2
Kutay, F.3
-
4
-
-
84956802323
-
A tutorial survey of architectures, algorithms, and applications for deep learning-ERRATUM
-
Deng L. A tutorial survey of architectures, algorithms, and applications for deep learning-ERRATUM. APSIPA Transactions on Signal and Information Processing. 2014; 3. https://doi.org/10.1017/atsip. 2013.9
-
(2014)
APSIPA Transactions on Signal and Information Processing
, Issue.3
-
-
Deng, L.1
-
5
-
-
84957926960
-
A survey of randomized algorithms for training neural networks
-
Zhang L, Suganthan PN. A survey of randomized algorithms for training neural networks. Information Sciences. 2016; 364-365(Supplement C):146-155. https://doi.org/10.1016/j.ins.2016.01.039.
-
(2016)
Information Sciences
, vol.364-365
, pp. 146-155
-
-
Zhang, L.1
Suganthan, P.N.2
-
6
-
-
85022083603
-
Extreme learning machine based transfer learning algorithms: A survey
-
Salaken SM, Khosravi A, Nguyen T, Nahavandi S. Extreme learning machine based transfer learning algorithms: A survey. Neurocomputing. 2017; 267:516-524. https://doi.org/10.1016/j.neucom.2017.06. 037.
-
(2017)
Neurocomputing
, vol.267
, pp. 516-524
-
-
Salaken, S.M.1
Khosravi, A.2
Nguyen, T.3
Nahavandi, S.4
-
7
-
-
85030639721
-
Machine learning approaches for estimating commercial building energy consumption
-
Robinson C, Dilkina B, Hubbs J, Zhang W, Guhathakurta S, Brown MA, et al. Machine learning approaches for estimating commercial building energy consumption. Applied Energy. 2017; 208(Supplement C):889-904. https://doi.org/10.1016/j.apenergy.2017.09.060.
-
(2017)
Applied Energy
, vol.208
, pp. 889-904
-
-
Robinson, C.1
Dilkina, B.2
Hubbs, J.3
Zhang, W.4
Guhathakurta, S.5
Brown, M.A.6
-
8
-
-
85008622769
-
Machine learning methods for solar radiation forecasting: A review
-
Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, et al. Machine learning methods for solar radiation forecasting: A review. Renewable Energy. 2017; 105(Supplement C):569-582. https://doi.org/10.1016/j.renene.2016.12.095.
-
(2017)
Renewable Energy
, vol.105
, pp. 569-582
-
-
Voyant, C.1
Notton, G.2
Kalogirou, S.3
Nivet, M.L.4
Paoli, C.5
Motte, F.6
-
9
-
-
0000032342
-
How effective are neural networks at forecasting and prediction? A review and evaluation
-
Adya M, Collopy F. How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting. 1998; 17(56):481-495. https://doi.org/10.1002/(SICI)1099-131X (1998090)17:5/6%3C481::AID-FOR709%3E3.0.CO;2-Q
-
(1998)
Journal of Forecasting
, vol.17
, Issue.56
, pp. 481-495
-
-
Adya, M.1
Collopy, F.2
-
10
-
-
35248833159
-
Neural networks Forecasting breakthrough or passing fad?
-
Chatfield C. Neural networks: Forecasting breakthrough or passing fad? International Journal of Forecasting. 1993; 9(1):1-3. http://dx.doi.org/10.1016/0169-2070(93)90043-M.
-
(1993)
International Journal of Forecasting
, vol.9
, Issue.1
, pp. 1-3
-
-
Chatfield, C.1
-
11
-
-
0004393134
-
Connectionist approach to time series prediction: An empirical test
-
Sharda R, Patil RB. Connectionist approach to time series prediction: An empirical test. Journal of Intelligent Manufacturing. 1992; 3(1):317-323. https://doi.org/10.1007/BF01577272
-
(1992)
Journal of Intelligent Manufacturing
, vol.3
, Issue.1
, pp. 317-323
-
-
Sharda, R.1
Patil, R.B.2
-
12
-
-
79956357674
-
Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction
-
Crone SF, Hibon M, Nikolopoulos K. Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting. 2011; 27 (3):635-660. http://dx.doi.org/10.1016/j.ijforecast.2011.04.001.
-
(2011)
International Journal of Forecasting
, vol.27
, Issue.3
, pp. 635-660
-
-
Crone, S.F.1
Hibon, M.2
Nikolopoulos, K.3
-
13
-
-
0042711018
-
On the need for time series data mining benchmarks: A survey and empirical demonstration
-
Keogh E, Kasetty S. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Mining and Knowledge Discovery. 2003; 7(4):349-371. https://doi.org/10.1023/A:1024988512476
-
(2003)
Data Mining and Knowledge Discovery
, vol.7
, Issue.4
, pp. 349-371
-
-
Keogh, E.1
Kasetty, S.2
-
15
-
-
77956724793
-
An empirical comparison of machine learning models for time series forecasting
-
Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H. An Empirical Comparison of Machine Learning Models for Time Series Forecasting. Econometric Reviews. 2010; 29(5-6):594-621. https://doi.org/10.1080/07474938.2010.481556
-
(2010)
Econometric Reviews
, vol.29
, Issue.5-6
, pp. 594-621
-
-
Ahmed, N.K.1
Atiya, A.F.2
Gayar, N.E.3
El-Shishiny, H.4
-
16
-
-
0034288942
-
The M3-Competition: Results, conclusions and implications
-
Makridakis S, Hibon M. The M3-Competition: results, conclusions and implications. International Journal of Forecasting. 2000; 16(4):451-476. https://doi.org/10.1016/S0169-2070(00)00057-1
-
(2000)
International Journal of Forecasting
, vol.16
, Issue.4
, pp. 451-476
-
-
Makridakis, S.1
Hibon, M.2
-
18
-
-
85016420579
-
Forecasting stochastic neural network based on financial empirical mode decomposition
-
PMID: 28364677
-
Wang J, Wang J. Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Networks. 2017; 90:8-20. https://doi.org/10.1016/j.neunet.2017.03.004. PMID: 28364677
-
(2017)
Neural Networks
, vol.90
, pp. 8-20
-
-
Wang, J.1
Wang, J.2
-
19
-
-
84927749443
-
Neural networks in business time series forecasting: Benefits and problems
-
Zhao L. Neural Networks In Business Time Series Forecasting: Benefits And Problems. Review of Business Information Systems (RBIS). 2009; 13(3):57-62.
-
(2009)
Review of Business Information Systems (RBIS)
, vol.13
, Issue.3
, pp. 57-62
-
-
Zhao, L.1
-
20
-
-
0034288490
-
The theta model: A decomposition approach to forecasting
-
Assimakopoulos V, Nikolopoulos K. The theta model: a decomposition approach to forecasting. International Journal of Forecasting. 2000; 16(4):521-530. https://doi.org/10.1016/S0169-2070(00)00066-2
-
(2000)
International Journal of Forecasting
, vol.16
, Issue.4
, pp. 521-530
-
-
Assimakopoulos, V.1
Nikolopoulos, K.2
-
21
-
-
56349104962
-
-
Ilies I, Jaeger H, Kosuchinas O, Rincon M, Vakänas V, Vaskevicius N. Stepping forward through echoes of the past: Forecasting with Echo State Networks, Technical Report: Jacobs University Bremen; 2007.
-
(2007)
Stepping Forward Through Echoes of the Past: Forecasting with Echo State Networks Technical Report: Jacobs University Bremen
-
-
Ilies, I.1
Jaeger, H.2
Kosuchinas, O.3
Rincon, M.4
Vakänas, V.5
Vaskevicius, N.6
-
22
-
-
33749523557
-
Exponential smoothing: The state of the art-Part II
-
Gardner ES. Exponential smoothing: The state of the art-Part II. International Journal of Forecasting. 2006; 22(4):637-666. https://doi.org/10.1016/j.ijforecast.2006.03.005
-
(2006)
International Journal of Forecasting
, vol.22
, Issue.4
, pp. 637-666
-
-
Gardner, E.S.1
-
24
-
-
84982135146
-
Predicting the direction of stock market index movement using an optimized artificial neural network model
-
Qiu M, Song Y. Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model. PLOS ONE. 2016; 11(5):1-11. https://doi.org/10.1371/journal.pone.0155133
-
(2016)
PLOS ONE
, vol.11
, Issue.5
, pp. 1-11
-
-
Qiu, M.1
Song, Y.2
-
25
-
-
84947289699
-
Forecasting macroeconomic variables using neural network models and three automated model selection techniques
-
Kock AB, Terä svirta T. Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques. Econometric Reviews. 2016; 35(8-10):1753-1779. https://doi.org/10.1080/07474938.2015.1035163
-
(2016)
Econometric Reviews
, vol.35
, Issue.8-10
, pp. 1753-1779
-
-
Kock, A.B.1
Teräsvirta, T.2
-
26
-
-
85020064625
-
Neural networks versus box-jenkins method for turnover forecasting: A case study on the romanian organisation
-
Gabor MR, Dorgo LA. Neural Networks Versus Box-Jenkins Method for Turnover Forecasting: a Case Study on the Romanian Organisation. Transformations in Business and Economics. 2017; 16(1):187-211.
-
(2017)
Transformations in Business and Economics
, vol.16
, Issue.1
, pp. 187-211
-
-
Gabor, M.R.1
Dorgo, L.A.2
-
28
-
-
4344591889
-
Neural network forecasting for seasonal and trend time series
-
Zhang GP, Qi M. Neural network forecasting for seasonal and trend time series. European Journal of Operational Research. 2005; 160(2):501-514. https://doi.org/10.1016/j.ejor.2003.08.037
-
(2005)
European Journal of Operational Research
, vol.160
, Issue.2
, pp. 501-514
-
-
Zhang, G.P.1
Qi, M.2
-
31
-
-
33749517168
-
Another look at measures of forecast accuracy
-
Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. International Journal of Forecasting. 2006; 22(4):679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001
-
(2006)
International Journal of Forecasting
, vol.22
, Issue.4
, pp. 679-688
-
-
Hyndman, R.J.1
Koehler, A.B.2
-
32
-
-
0007104074
-
On the asymmetry of the symmetric MAPE
-
Goodwin P, Lawton R. On the asymmetry of the symmetric MAPE. International Journal of Forecasting. 1999; 15(4):405-408. http://dx.doi.org/10.1016/S0169-2070(99)00007-2.
-
(1999)
International Journal of Forecasting
, vol.15
, Issue.4
, pp. 405-408
-
-
Goodwin, P.1
Lawton, R.2
-
34
-
-
84984477151
-
Exponential smoothing: The state of the art
-
Gardner ES. Exponential smoothing: the state of the art. Journal of Forecasting. 1985; 4(1):1-28. https://doi.org/10.1002/for.3980040103
-
(1985)
Journal of Forecasting
, vol.4
, Issue.1
, pp. 1-28
-
-
Gardner, E.S.1
-
35
-
-
79956363043
-
Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition
-
Andrawis RR, Atiya AF, El-Shishiny H. Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition. International Journal of Forecasting. 2011; 27 (3):672-688. https://doi.org/10.1016/j.ijforecast.2010.09.005
-
(2011)
International Journal of Forecasting
, vol.27
, Issue.3
, pp. 672-688
-
-
Andrawis, R.R.1
Atiya, A.F.2
El-Shishiny, H.3
-
36
-
-
48749112805
-
Automatic time series forecasting: The forecast package for R
-
Hyndman R, Khandakar Y. Automatic time series forecasting: the forecast package for R. Journal of Statistical Software. 2008; 26(3):1-22.
-
(2008)
Journal of Statistical Software
, vol.26
, Issue.3
, pp. 1-22
-
-
Hyndman, R.1
Khandakar, Y.2
-
37
-
-
0036071568
-
A state space framework for automatic forecasting using exponential smoothing methods
-
Hyndman RJ, Koehler AB, Snyder RD, Grose S. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting. 2002; 18(3):439-454. https://doi.org/10.1016/S0169-2070(01)00110-8
-
(2002)
International Journal of Forecasting
, vol.18
, Issue.3
, pp. 439-454
-
-
Hyndman, R.J.1
Koehler, A.B.2
Snyder, R.D.3
Grose, S.4
-
38
-
-
0028401357
-
Recurrent neural networks and robust time series prediction
-
PMID: 18267794
-
Connor JT, Martin RD, Atlas LE. Recurrent neural networks and robust time series prediction. IEEE transactions on neural networks. 1994; 5(2):240-54. https://doi.org/10.1109/72.279188 PMID: 18267794
-
(1994)
IEEE Transactions on Neural Networks
, vol.5
, Issue.2
, pp. 240-254
-
-
Connor, J.T.1
Martin, R.D.2
Atlas, L.E.3
-
41
-
-
0031573117
-
Long short-term memory
-
PMID: 9377276
-
Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation. 1997; 9(8):1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735 PMID: 9377276
-
(1997)
Neural Computation
, vol.9
, Issue.8
, pp. 1735-1780
-
-
Hochreiter, S.1
Schmidhuber, J.2
-
42
-
-
0023331258
-
An introduction to computing with neural nets
-
Lippmann RP. An Introduction to Computing with Neural Nets. IEEE ASSP Magazine. 1987; 4(2):4-22. https://doi.org/10.1109/MASSP.1987.1165576
-
(1987)
IEEE ASSP Magazine
, vol.4
, Issue.2
, pp. 4-22
-
-
Lippmann, R.P.1
-
43
-
-
0027205884
-
A scaled conjugate gradient algorithm for fast supervised learning
-
Młller M. A scaled conjugate gradient algorithm for fast supervised learning. Neural networks. 1993; 6:525-533. https://doi.org/10.1016/S0893-6080(05)80056-5
-
(1993)
Neural Networks
, vol.6
, pp. 525-533
-
-
Młller, M.1
-
44
-
-
84893467682
-
Neural network ensemble operators for time series forecasting
-
Kourentzes N, Barrow DK, Crone SF. Neural network ensemble operators for time series forecasting. Expert Systems with Applications. 2014; 41(9):4235-4244. https://doi.org/10.1016/j.eswa.2013.12.011
-
(2014)
Expert Systems with Applications
, vol.41
, Issue.9
, pp. 4235-4244
-
-
Kourentzes, N.1
Barrow, D.K.2
Crone, S.F.3
-
45
-
-
84857383259
-
Neural networks in r using the stuttgart neural network simulator: Rsnns
-
Bergmeir C, Benitez JM. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software. 2012; 46(7):1-26. https://doi.org/10.18637/jss.v046.i07
-
(2012)
Journal of Statistical Software
, vol.46
, Issue.7
, pp. 1-26
-
-
Bergmeir, C.1
Benitez, J.M.2
-
46
-
-
0001025418
-
Bayesian interpolation
-
MacKay DJC. Bayesian Interpolation. Neural Computation. 1992; 4(3):415-447. https://doi.org/10.1162/neco.1992.4.3.415
-
(1992)
Neural Computation
, vol.4
, Issue.3
, pp. 415-447
-
-
MacKay, D.J.C.1
-
48
-
-
0025536870
-
Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
-
Nguyen D, Widrow B. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. IJCNN Int Joint Conf Neural Networks. 1990; 13:C21.
-
(1990)
IJCNN Int Joint Conf Neural Networks
, vol.13
, pp. 21
-
-
Nguyen, D.1
Widrow, B.2
-
50
-
-
0026254768
-
A general regression neural network
-
PMID: 18282872
-
Specht DF. A general regression neural network. IEEE Transactions on Neural Networks. 1991; 2 (6):568-576. https://doi.org/10.1109/72.97934 PMID: 18282872
-
(1991)
IEEE Transactions on Neural Networks
, vol.2
, Issue.6
, pp. 568-576
-
-
Specht, D.F.1
-
56
-
-
84940412877
-
-
Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F. e1071: Misc Functions of the Department of Statistics, Probability Theory Group, TU Wien; 2017. Available from: https://cran.r-project.org/package=e1071.
-
(2017)
E1071: Misc Functions of the Department of Statistics, Probability Theory Group, TU Wien
-
-
Meyer, D.1
Dimitriadou, E.2
Hornik, K.3
Weingessel, A.4
Leisch, F.5
-
58
-
-
11244352554
-
Kernlab-an s4 package for kernel methods in r
-
Karatzoglou A, Smola A, Hornik K, Zeileis A. kernlab-An S4 Package for Kernel Methods in R. Journal of Statistical Software. 2004; 11(9):1-20. https://doi.org/10.18637/jss.v011.i09
-
(2004)
Journal of Statistical Software
, vol.11
, Issue.9
, pp. 1-20
-
-
Karatzoglou, A.1
Smola, A.2
Hornik, K.3
Zeileis, A.4
-
59
-
-
26444565569
-
Finding structure in time
-
Elman JL. Finding structure in time. Cognitive Science. 1990; 14(2):179-211. https://doi.org/10.1207/s15516709cog1402-1
-
(1990)
Cognitive Science
, vol.14
, Issue.2
, pp. 179-211
-
-
Elman, J.L.1
-
60
-
-
84971640658
-
-
Chollet F, et al. Keras; 2015. https://github.com/keras-team/keras.
-
(2015)
Keras
-
-
Chollet, F.1
-
61
-
-
84958264664
-
-
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems; 2015. Available from: http://tensorflow.org/.
-
(2015)
TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
-
-
Abadi, M.1
Agarwal, A.2
Barham, P.3
Brevdo, E.4
Chen, Z.5
Citro, C.6
-
62
-
-
0003053374
-
Research prospective on neural network forecasting
-
Gorr WL. Research prospective on neural network forecasting. International Journal of Forecasting. 1994; 10(1):1-4. https://doi.org/10.1016/0169-2070(94)90044-2
-
(1994)
International Journal of Forecasting
, vol.10
, Issue.1
, pp. 1-4
-
-
Gorr, W.L.1
-
63
-
-
0028055346
-
Canneural networks applied to time series forecasting learn seasonal patterns: An empirical investigation
-
Nelson M, Hill T, Remus B, O'Connor M. Can neural networks applied to time series forecasting learn seasonal patterns: an empirical investigation. System Sciences, 1994 Proceedings of the Twenty-Seventh Hawaii International Conference on. 1994; 3:649-655. https://doi.org/10.1109/HICSS.1994. 323316
-
(1994)
System Sciences 1994 Proceedings of the Twenty-Seventh Hawaii International Conference on
, vol.3
, pp. 649-655
-
-
Nelson, M.1
Hill, T.2
Remus, B.3
O'Connor, M.4
-
64
-
-
0029409251
-
Neural modeling for time series: A statistical stepwise method for weight elimination
-
PMID: 18263428
-
Cottrell M, Girard B, Girard Y, Mangeas M, Muller C. Neural Modeling for Time Series: A Statistical Stepwise Method for Weight Elimination. IEEE Transactions on Neural Networks. 1995; 6(6):1355-1364. https://doi.org/10.1109/72.471372 PMID: 18263428
-
(1995)
IEEE Transactions on Neural Networks
, vol.6
, Issue.6
, pp. 1355-1364
-
-
Cottrell, M.1
Girard, B.2
Girard, Y.3
Mangeas, M.4
Muller, C.5
-
65
-
-
49049143455
-
Trends and random walks in macroeconmic time series. Some evidence and implications
-
Nelson CR, Plosser CR. Trends and random walks in macroeconmic time series. Some evidence and implications. Journal of Monetary Economics. 1982; 10(2):139-162. https://doi.org/10.1016/0304-3932 (82)90012-5
-
(1982)
Journal of Monetary Economics
, vol.10
, Issue.2
, pp. 139-162
-
-
Nelson, C.R.1
Plosser, C.R.2
-
67
-
-
0002605320
-
Some quick sign tests for trend in location and dispersion
-
Cox DR, Stuart A. Some Quick Sign Tests for Trend in Location and Dispersion. Biometrika. 1955; 42(1-2):80-95. https://doi.org/10.2307/2333424
-
(1955)
Biometrika
, vol.42
, Issue.1-2
, pp. 80-95
-
-
Cox, D.R.1
Stuart, A.2
-
74
-
-
84866454518
-
On the forecasting accuracy of multivariate GARCH models
-
Laurent S, Rombouts JVK, Violante F. On the forecasting accuracy of multivariate GARCH models. Journal of Applied Econometrics. 2012; 27(6):934-955. https://doi.org/10.1002/jae.1248
-
(2012)
Journal of Applied Econometrics
, vol.27
, Issue.6
, pp. 934-955
-
-
Laurent, S.1
Rombouts, J.V.K.2
Violante, F.3
-
75
-
-
84930272613
-
Simple versus complex forecasting: The evidence
-
Green KC, Armstrong JS. Simple versus complex forecasting: The evidence. Journal of Business Research. 2015; 68(8):1678-1685. http://dx.doi.org/10.1016/j.jbusres.2015.03.026.
-
(2015)
Journal of Business Research
, vol.68
, Issue.8
, pp. 1678-1685
-
-
Green, K.C.1
Armstrong, J.S.2
-
78
-
-
85009115385
-
Visualising forecasting algorithm performance using time series instance spaces
-
Kang Y, Hyndman RJ, Smith-Miles K. Visualising forecasting algorithm performance using time series instance spaces. International Journal of Forecasting. 2017; 33(2):345-358. https://doi.org/10.1016/j.ijforecast.2016.09.004
-
(2017)
International Journal of Forecasting
, vol.33
, Issue.2
, pp. 345-358
-
-
Kang, Y.1
Hyndman, R.J.2
Smith-Miles, K.3
-
79
-
-
84898804905
-
Horses for Courses' in demand forecasting
-
Petropoulos F, Makridakis S, Assimakopoulos V, Nikolopoulos K. Horses for Courses' in demand forecasting. European Journal of Operational Research. 2014; 237(1):152-163. https://doi.org/10.1016/j.ejor.2014.02.036
-
(2014)
European Journal of Operational Research
, vol.237
, Issue.1
, pp. 152-163
-
-
Petropoulos, F.1
Makridakis, S.2
Assimakopoulos, V.3
Nikolopoulos, K.4
-
80
-
-
85010651075
-
A survey of deep neural network architectures and their applications
-
Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing. 2017; 234(Supplement C):11-26. https://doi.org/10.1016/j.neucom.2016.12.038.
-
(2017)
Neurocomputing
, vol.234
, pp. 11-26
-
-
Liu, W.1
Wang, Z.2
Liu, X.3
Zeng, N.4
Liu, Y.5
Alsaadi, F.E.6
-
81
-
-
85029156229
-
Facial expression recognition via learning deep sparse autoencoders
-
Zeng N, Zhang H, Song B, Liu W, Li Y, Dobaie AM. Facial expression recognition via learning deep sparse autoencoders. Neurocomputing. 2018; 273(Supplement C):643-649. https://doi.org/10.1016/j.neucom.2017.08.043.
-
(2018)
Neurocomputing
, vol.273
, pp. 643-649
-
-
Zeng, N.1
Zhang, H.2
Song, B.3
Liu, W.4
Li, Y.5
Dobaie, A.M.6
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