Items where Subject is "C45 - Neural Networks and Related Topics"

English

ANDREOU, A. S. and PARSOPOULOS, K. E. and VRACHATIS, M. N. and Zombanakis, George A. (2002): Searching for the Optimal Defence Expenditure: An Answer in the Context of the Greek – Turkish Arms Race. Published in: Financial Engineering, E- Commerce and Supply Chain , Vol. 1, No. 1 (14 September 2002): pp. 1-24.

Abounoori, Abbas Ali and Mohammadali, Hanieh and Gandali Alikhani, Nadiya and Naderi, Esmaeil (2012): Comparative study of static and dynamic neural network models for nonlinear time series forecasting.

Abounoori, Abbas Ali and Naderi, Esmaeil and Gandali Alikhani, Nadiya and Amiri, Ashkan (2013): Financial Time Series Forecasting by Developing a Hybrid Intelligent System. Published in: European Journal of Scientific Research , Vol. 98, No. 4 (4 March 2013): pp. 10-20.

Abounoori, Abbas Ali and Naderi, Esmaeil and Gandali Alikhani, Nadiya and Amiri, Ashkan (2013): Financial Time Series Forecasting by Developing a Hybrid Intelligent System. Published in: European Journal of Scientific Research , Vol. 98, No. 4 (4 March 2013): pp. 529-541.

Amavilah, Voxi Heinrich and Otero, Abraham and Andres, Antonio Rodriguez (2021): Knowledge Economy Classification in African Countries: A Model-Based Clustering Approach. Published in: Information Technology for Development No. DOI: 10.1080/02681102.2021.1950597 (16 July 2021)

Andreou, Andreas S. and Zombanakis, George A. (2000): Financial Versus Human Resources in the Greek-Turkish Arms Race: A Forecasting Investigation Using Artificial Neural Networks. Published in: Defence and Peace Economics , Vol. 11, No. 4 (2000): pp. 403-426.

Andreou, Andreas S. and Zombanakis, George A. (2010): Financial vs human resources in the Greek-Turkish arms race 10 years on. Published in: Defence and Peace Economics , Vol. Vol. 2, No. No. 4, 2011 : pp. 1-11.

Andreou, Andreas S. and Zombanakis, George A. (2003): Intelligent information systems for defence problems. Published in: (14 July 2003): pp. 1-163.

Andreou, Andreas S. and Zombanakis, George A. (2001): A Neural Network Measurement of Relative Military Security: The Case of Greece and Cyprus. Published in: Defence and Peace Economics , Vol. 12, No. 4 (2001): pp. 303-324.

Andreou, Andreas S. and Zombanakis, George A. and Georgopoulos, E. F. and Likothanassis, S. D. (1998): Forecasting Exchange-Rates via Local Approximation Methods and Neural Networks. Published in: European Research Studies , Vol. 1, No. 4 (December 1998): pp. 5-33.

Andreou, Andreas S. and Zombanakis, George A. and Georgopoulos, E. F. and Likothanassis, S. D. (2000): In Search of a Warning Strategy Against Exchange-rate Attacks: Forecasting Tactics Using Artificial Neural Networks. Published in: Discrete Dynamics in Nature and Society , Vol. 5, No. 2 : pp. 121-137.

Andres, Antonio Rodriguez and Otero, Abraham and Amavilah, Voxi Heinrich (2021): Evaluation of technology clubs by clustering: A cautionary note. Forthcoming in: Applied Economics (2021)

Andres, Antonio Rodriguez and Otero, Abraham and Amavilah, Voxi Heinrich (2021): Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies. Published in: Expert Systems with Applications , Vol. 184, No. https://doi.org/10.1016/j.eswa.2021.115514 (1 December 2021)

Antipov, Evgeny and Pokryshevskaya, Elena (2010): Applying a CART-based approach for the diagnostics of mass appraisal models.

Antipov, Evgeny and Pokryshevskaya, Elena (2010): Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics.

Basihos, Seda (2016): Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey.

Benkovich, Nikita and Dedenok, Roman and Golubev, Dmitry (2019): Deep Quarantine for Suspicious Mail. Published in: CEUR Workshop Proceedings , Vol. 2479, (26 September 2019): pp. 68-76.

Bildirici, Melike and Ersin, Özgür (2012): Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models.

Brummelhuis, Raymond and Luo, Zhongmin (2018): Arbitrage Opportunities in CDS Term Structure: Theory and Implications for OTC Derivatives.

Brummelhuis, Raymond and Luo, Zhongmin (2019): Bank Net Interest Margin Forecasting and Capital Adequacy Stress Testing by Machine Learning Techniques.

Brummelhuis, Raymond and Luo, Zhongmin (2017): CDS Rate Construction Methods by Machine Learning Techniques.

Bucci, Andrea (2019): Cholesky-ANN models for predicting multivariate realized volatility.

Bucci, Andrea (2019): Realized Volatility Forecasting with Neural Networks.

Chan, Tze-Haw and Lye, Chun Teck and Hooy, Chee-Wooi (2010): Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work?

Chen, Ying and Grith, Maria and Lai, Hannah L. H. (2023): Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach.

Cifter, Atilla and Ozun, Alper (2007): The Effects of International F/X Markets on Domestic Currencies Using Wavelet Networks: Evidence from Emerging Markets.

Cifter, Atilla and Ozun, Alper (2007): Estimating the Effects of Interest Rates on Share Prices Using Multi-scale Causality Test in Emerging Markets: Evidence from Turkey.

Ciuiu, Daniel (2008): Pattern classification using polynomial and linear regression. Published in: Proceedings of International Conference Trends and Challenges in Applied Mathematics (2008): pp. 153-156.

Ciuiu, Daniel (2008): Pattern classification using principal components regression. Published in: Proceedings of International Conference Trends and Challenges in Applied Mathematics (2008): pp. 149-152.

Delavari, Majid and Gandali Alikhani, Nadiya and Naderi, Esmaeil (2012): Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting? Published in: International Journal of Economics and Financial Issues , Vol. 3, No. 2 (10 April 2013): pp. 447-465.

Dev, Pritha (2010): Identity and Fragmentation in Networks.

Diunugala, Hemantha Premakumara and Mombeuil, Claudel (2020): Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods. Published in: Journal of Tourism, Heritage & Services Marketing , Vol. 6, No. 3 (30 October 2020): pp. 3-13.

Djennas, Meriem and Benbouziane, Mohamed and Djennas, Mustapha (2012): Agent-Based Modeling in Supply Chain Management:A Genetic Algorithm and Fuzzy Logic Approach. Published in: International Journal of Artificial Intelligence & Applications (IJAIA) , Vol. Vol.3,, No. No.5 (September 2012): pp. 13-30.

Fajar, Muhammad (2019): An application of hybrid forecasting singular spectrum analysis – extreme learning machine method in foreign tourists forecasting. Published in: Jurnal Matematika MANTIK , Vol. 5, No. 2 (31 October 2019): pp. 60-68.

Fajar, Muhammad and Hartini, Sri (2020): Comparison of ARIMA, SSA, and ARIMA – SSA hybrid model performance in Indonesian economic growth forecasting. Published in: The 2020 Asia-Pacific Statistics Week (16 June 2020)

Fajar, Muhammad and Hartini, Sri (2017): Inflation forecasting by hybrid singular spectrum analysis – multilayer perceptrons neural network method, case of Indonesia. Forthcoming in:

Filippou, Miltiades and Zervopoulos, Panagiotis (2011): Developing a hybrid comparative optimization model for short-term forecasting: an ‘idle time interval’ roadmap for operational units’ strategic planning.

Filippou, Miltiades and Zervopoulos, Panagiotis (2011): Developing a short-term comparative optimization forecasting model for operational units’ strategic planning.

Fischer, Manfred M. (1992): Expert Systems and Artificial Neural Networks for Spatial Analysis and Modelling. Essential Components for Knowledge-Based Geographical Information Systems. Published in: Geographical Systems , Vol. 1, No. 3 (1994): pp. 221-235.

Fischer, Manfred M. (2002): Learning in Neural Spatial Interaction Models: A Statistical Perspective. Published in: Journal of Geographical Systems , Vol. 4, No. 3 (2002): pp. 287-299.

Fischer, Manfred M. (2006): Neural Networks. A General Framework for Non-Linear Function Approximation. Published in: Transactions in GIS , Vol. 10, No. 4 (2006)

Fischer, Manfred M. and Gopal, Sucharita (1994): Artificial Neural Networks. A New Approach to Modelling Interregional Telecommunication Flows. Published in: Journal of Regional Science , Vol. 34, No. 4 (1994): pp. 503-527.

Fischer, Manfred M. and Gopal, Sucharita and Staufer, Petra and Steinnocher, Klaus (1995): Evaluation of Neural Pattern Classifiers for a Remote Sensing Application. Published in: Geographical Systems , Vol. 4, No. 2 (1997): pp. 195-226.

Fischer, Manfred M. and Reismann, Martin (2000): Evaluating Neural Spatial Interaction Modelling by Bootstrapping. Published in: Networks and Spatial Economics , Vol. 2, No. 3 (2002): pp. 255-268.

Fischer, Manfred M. and Reismann, Martin (2002): A Methodology for Neural Spatial Interaction Modeling. Published in: Geographical Analysis , Vol. 34, No. 3 (2002): pp. 207-228.

Fischer, Manfred M. and Staufer, Petra (1998): Optimization in an Error Backpropagation Neural Network Environment with a Performance Test on a Pattern Classification Problem. Published in: Geographical Analysis , Vol. 31, No. 3 (1999): pp. 89-108.

Gawlik, Remigiusz (2014): Application of Artificial Intelligence Methods for Analysis of Material and Non-material Determinants of Functioning of Young Europeans in Times of Crisis in the Eurozone. Published in: Global and Regional Implications of the Euro Area Crisis No. E. Molendowski, P. Stanek (Eds.), Warszawa: PWN (2014): pp. 64-80.

Gawlik, Remigiusz (2013): Material and Non-material Determinants of European Youth's Life Quality. Published in: Globalizing Businesses for the Next Century: Visualizing and Developing Contemporary Approaches to Harness Future Opportunities (22 June 2013): pp. 339-346.

Gawlik, Remigiusz (2016): Methodological Aspects of Qualitative-Quantitative Analysis of Decision-Making Processes. Published in: Management and Production Engineering Review , Vol. 7, No. 2 (June 2016): pp. 3-11.

Gawlik, Remigiusz and Gołębiowski, Kamil (2014): Incorporating Qualitative Indicators of Well - Being into Quantitative Economic Research. Published in: Managing in an Interconnected World: Pioneering Business and Technology Excellence (2014): pp. 673-682.

Gelhausen, Marc Christopher (2007): A Generalized Neural Logit Model for Airport and Access Mode Choice in Germany. Published in: Proceedings of the 11th Air Transport Research Society World Conference (2007): pp. 1-42.

Gelhausen, Marc Christopher and Wilken, Dieter (2006): Airport and Access Mode Choice : A Generalized Nested Logit Model Approach. Published in: Proceedings of the 10th Air Transport Research Society World Conference (2006): pp. 1-30.

Giovanis, Eleftherios (2008): Additional Smoothing Transition Autoregressive Models.

Giovanis, Eleftherios (2008): Applications of Least Mean Square (LMS) Algorithm Regression in Time-Series Analysis.

Giovanis, Eleftherios (2009): Bootstrapping Fuzzy-GARCH Regressions on the Day of the Week Effect in Stock Returns: Applications in MATLAB.

Giovanis, Eleftherios (2008): Neuro-Fuzzy approach for the predictions of economic crisis.

Giovanis, Eleftherios (2008): Smoothing Transition Autoregressive (STAR) Models with Ordinary Least Squares and Genetic Algorithms Optimization.

Giovanis, Eleftherios (2012): Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA. Published in: Economic Analysis & Policy , Vol. 42, No. 1 (March 2012): pp. 79-95.

Giovanis, Eleftherios (2008): An algorithm using GARCH process , Monte-Carlo simulation and wavelets analysis for stock prediction.

Giovanis, Eleftherios (2008): A panel data analysis for the greenhouse effects in fifteen countries of European Union.

Giovanis, eleftheios (2008): A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods.

Gomez-Ruano, Gerardo (2020): Data Science: A Primer for Economists.

Grilli, Luca and Santoro, Domenico (2020): Generative Adversarial Network for Market Hourly Discrimination.

Grilli, Luca and Santoro, Domenico (2020): How Boltzmann Entropy Improves Prediction with LSTM.

Hollenbeck, Brett and Taylor, Wayne (2021): Leveraging Loyalty Programs Using Competitor Based Targeting. Published in: Quantitative Marketing and Economics

Hossain, Md. Mobarak and Chowdhury, Md Niaz Murshed (2019): Econometric Ways to Estimate the Age and Price of Abalone.

Jackwerth, Jens Carsten (1997): Artificial Stupidity: A Reply. Published in: Journal of Portfolio Management , Vol. 27, No. 1 : pp. 120-121.

K. K., Suresh and K., Pradeepa Veerakumari (2007): Construction and Evaluation of Performance Measures for Bayesian Chain Sampling Plan (BChSP-1). Published in: Acta Ciencia Indica , Vol. 33, No. 4 (2007): pp. 16-35.

Kadyrov, Timur and Ignatov, Dmitry I. (2019): Attribution of Customers’ Actions Based on Machine Learning Approach. Published in: CEUR Workshop Proceedings , Vol. 2479, (26 September 2019): pp. 77-88.

Kahloul, Ines and Ben Mabrouk, Anouar and Hallara, Salah-Eddine (2009): Wavelet-Based Prediction for Governance, Diversi cation and Value Creation Variables.

Kaizoji, Taisei (2012): A Note on Stability of Self-Consistent Equilibrium in an Asynchronous Model of Discrete-Choice with Social Interaction.

Katsafados, Apostolos G. and Leledakis, George N. and Pyrgiotakis, Emmanouil G. and Androutsopoulos, Ion and Fergadiotis, Manos (2021): Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach.

Kitova, Olga and Dyakonova, Ludmila and Savinova, Victoria and Fomin, Kiril (2023): Forecasting the main economic indicators for industry in the analytical system "Horizon".

Kitova, Olga and Dyakonova, Ludmila and Savinova, Victoria (2020): Prediction of Socio-Economic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon”.

Kitova, Olga and Savinova, Victoria (2021): Development of an Ensemble of Models for Predicting Socio-Economic Indicators of the Russian Federation using IRT-Theory and Bagging Methods.

Klein, Achim and Urbig, Diemo and Kirn, Stefan (2008): Who drives the Market? Estimating a heterogeneous Agent-based Financial Market Model using a Neural Network Approach.

Korobilis, Dimitris and Shimizu, Kenichi (2021): Bayesian Approaches to Shrinkage and Sparse Estimation.

Kulaksizoglu, Tamer (2015): Measuring the Core Inflation in Turkey with the SM-AR Model.

Leiva-Leon, Danilo (2013): A New Approach to Infer Changes in the Synchronization of Business Cycle Phases.

Leonidas, Spiliopoulos (2009): Learning backward induction: a neural network agent approach. Published in: Agent-Based Approaches in Economic and Social Complex Systems VI, edn. 2011, Springer, Japan (2011)

Liu, Mengxiao and Wang, Luhang and Yi, Yimin (2022): Quality Innovation, Cost Innovation, Export, and Firm Productivity Evolution: Evidence from the Chinese Electronics Industry.

Malliaris, A.G. and Malliaris, Mary (2011): Are foreign currency markets interdependent? evidence from data mining technologies. Forthcoming in: Stochastics: Finance and Risk No. 2012

Mateou, Nicos H. and Zombanakis, George A. (2009): Fuzzy cognitive maps face the question of the Greek current account deficit sustainability.

Matkovskyy, Roman (2012): Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks.

Matkovskyy, Roman and Bouraoui, Taoufik and Hammami, Helmi (2015): Estimation and prediction of an Index of Financial Safety of Tunisia. Published in: Research in International Business and Finance , Vol. 38, (September 2016): pp. 485-493.

Mengov, George and Egbert, Henrik and Pulov, Stefan and Georgiev, Kalin (2008): Emotional Balances in Experimental Consumer Choice. Published in: Neural Networks , Vol. 21, No. 9 (1 September 2008): pp. 1213-1219.

Mestiri, Sami (2024): Financial applications of machine learning using R software.

Mishra, SK (2009): The most representative composite rank ordering of multi-attribute objects by the particle swarm optimization.

Mishra, SK (2014): A note on Poincaré recurrence in Anosov diffeomorphic transformation of discretized outline of some plant leaves.

Mishra, Sudhanshu K (2014): What happens if in the principal component analysis the Pearsonian is replaced by the Brownian coefficient of correlation?

Nazarian, Rafik and Gandali Alikhani, Nadiya and Naderi, Esmaeil and Amiri, Ashkan (2013): Forecasting Stock Market Volatility: A Forecast Combination Approach.

Nguefack-Tsague, Georges and Zucchini, Walter (2011): Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach.

Nowotarski, Jakub and Tomczyk, Jakub and Weron, Rafal (2012): Robust estimation and forecasting of the long-term seasonal component of electricity spot prices.

Oancea, Bogdan and Dragoescu, Raluca and Ciucu, Stefan (2013): Predicting students’ results in higher education using a neural network. Published in: International Conference on Applied Information and Communication Technologies (AICT2013 ) (April 2013): pp. 190-193.

Osipov, Vasiliy and Zhukova, Nataly and Miloserdov, Dmitriy (2019): Neural Network Associative Forecasting of Demand for Goods. Published in: CEUR Workshop Proceedings , Vol. 2479, (26 September 2019): pp. 100-108.

Ozun, Alper and Cifter, Atilla (2007): Modeling Long-Term Memory Effect in Stock Prices: A Comparative Analysis with GPH Test and Daubechies Wavelets.

Pena Centeno, Tonatiuh and Martinez Jaramillo, Serafin and Abudu, Bolanle (2009): Predicción de bancarrota: Una comparación de técnicas estadísticas y de aprendizaje supervisado para computadora.

Pereira, Robert (1999): Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-optimised Technical Trading Rules.

Pereira, Robert (2000): Genetic Algorithm Optimisation for Finance and Investments.

Photis, Yorgos N. and Grekoussis, George (2003): Assesing demand in stochastic locational planning problems: An Artificial Intelligence approach for emergency service systems. Published in: Conference Proceedings of the 2005 Conference on Computers in Urban Planning and Urban Management (CUPUM 05) , Vol. 373, No. 05 (2003): pp. 1-16.

Photis, Yorgos N. and Manetos, Panos and Grekoussis, George (2003): Modeling urban evolution by identifying spatiotemporal patterns and applying methods of artificial intelligence.Case study: Athens, Greece. Published in: Proceedings of the 3rd Euroconference: The European City in Transition , Vol. 1, No. 1 (29 November 2003): pp. 96-108.

Pugalendhi, Subburethina Bharathi and Nakkeeran, Senthil kumar (2011): Mind mapping management. Published in: The observer of Management Education , Vol. Volume, No. Issue 9 (1 September 2011): pp. 10-13.

Saltoglu, Burak and Yenilmez, Taylan (2010): Analyzing Systemic Risk with Financial Networks An Application During a Financial Crash.

Spiliopoulos, Leonidas (2009): Neural networks as a learning paradigm for general normal form games.

Su, EnDer and Fen, Yu-Gin (2011): Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market.

Sumi, P. Sobana and Delhibabu, Radhakrishnan (2019): Glioblastoma Multiforme Classification On High Resolution Histology Image Using Deep Spatial Fusion Network. Published in: CEUR Workshop Proceedings , Vol. 2479, (26 September 2019): pp. 109-120.

Temel, Tugrul and Phumpiu, Paul (2023): Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience.

Temel, Tugrul and Phumpiu, Paul (2023): Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience.

Tsionas, Efthymios G. and Michaelides, Panayotis G. and Vouldis, Angelos (2008): Neural Networks for Approximating the Cost and Production Functions.

Wilken, Dieter and Berster, Peter and Gelhausen, Marc Christopher (2005): Airport Choice in Germany - New Empirical Evidence of the German Air Traveller Survey 2003. Published in: Proceedings of the 9th Air Transport Research Society World Congress (2005): pp. 1-29.

Yang, Bill Huajian (2022): Modeling Path-Dependent State Transition by a Recurrent Neural Network. Forthcoming in: Big Data and Information Analytics

Zolnikov, Pavel and Zubov, Maxim and Nikitinsky, Nikita and Makarov, Ilya (2019): Efficient Algorithms for Constructing Multiplex Networks Embedding. Published in: CEUR Workshop Proceedings , Vol. 2479, (26 September 2019): pp. 57-67.

Zombanakis, George A. and Andreou, Andreas A. (2010): Financial versus human Resources in the Greek - Turkish Arms Race 10 Years on: A forecasting Investigation using Artificial Neural Networks. Published in: Defence and Peace Economics , Vol. 22, No. 4 : pp. 1-11.

de Rigo, Daniele (2013): Software uncertainty in integrated environmental modelling: the role of semantics and open science. Forthcoming in: Geophysical Research Abstracts , Vol. 15, (2013)

de Rigo, Daniele (2013): Software uncertainty in integrated environmental modelling: the role of semantics and open science. Forthcoming in: Geophysical Research Abstracts , Vol. 15, (2013)

de Rigo, Daniele and Caudullo, Giovanni and San-Miguel-Ayanz, Jesús and Barredo, José I. (2017): Robust modelling of the impacts of climate change on the habitat suitability of forest tree species. Published in: (March 2017)

de Rigo, Daniele and Corti, Paolo and Caudullo, Giovanni and McInerney, Daniel and Di Leo, Margherita and San-Miguel-Ayanz, Jesús (2013): Toward open science at the European scale: geospatial semantic array programming for integrated environmental modelling. Forthcoming in: Geophysical Research Abstracts , Vol. 15, (2013)

de Rigo, Daniele and Rizzoli, Andrea Emilio and Soncini-Sessa, Rodolfo and Weber, Enrico and Zenesi, Pietro (2001): Neuro-dynamic programming for the efficient management of reservoir networks. Published in: Proceedings of MODSIM 2001, International Congress on Modelling and Simulation , Vol. 4, (December 2001): pp. 1949-1954.

di Iasio, Giovanni and Battiston, Stefano and Infante, Luigi and Pierobon, Federico (2013): Capital and Contagion in Financial Networks.

du Jardin, Philippe (2008): Bankruptcy prediction and neural networks: The contribution of variable selection methods. Published in: Proceedings of the Second European Symposium on Time Series Prediction (Estsp 2008), Helsinki University of Technology, Porvoo, Finland, (2008): pp. 271-284.

du Jardin, Philippe (2009): Bankruptcy prediction models: How to choose the most relevant variables? Published in: Bankers, Markets & Investors No. 98 (January 2009): pp. 39-46.

du Jardin, Philippe (2010): Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy. Published in: Neurocomputing , Vol. 73, No. 10-12 (2010): pp. 2047-2060.

du Jardin, Philippe (2012): The influence of variable selection methods on the accuracy of bankruptcy prediction models. Published in: Bankers, Markets & Investors No. 116 (January 2012): pp. 20-39.

du Jardin, Philippe and Séverin, Eric (2010): Dynamic analysis of the business failure process: A study of bankruptcy trajectories. Published in: Proceedings of the 6th Portuguese Finance Network Conference, Ponta Delgada, Azores , Vol. 2010, (1 July 2010)

French

Mestiri, Sami (2023): How to use machine learning in finance.

German

Deetz, Marcus and Poddig, Thorsten and Varmaz, Armin (2009): Klassifizierung von Hedge-Fonds durch das k-means Clustering von Self-Organizing Maps: eine renditebasierte Analyse zur Selbsteinstufungsgüte und Stiländerungsproblematik.

Spanish

Gutierrez-Lythgoe, Antonio (2023): Autoempleo y Machine Learning: Una aplicación para España.

Gutierrez-Lythgoe, Antonio (2023): Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial.

Medel-Ramírez, Carlos and Medel-López, Hilario and Lara-Mérida, Jennifer (2021): (SARS-CoV-2) COVID 19: Vigilancia genómica y evaluación del impacto en la población hablante de lengua indígena en México.

Turkish

Cakir, Murat (2005): Firma Başarısızlığının Dinamiklerinin Belirlenmesinde Makina Öğrenmesi Teknikleri: Ampirik Uygulamalar ve Karşılaştırmalı Analiz.