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  • Search: subject:"Ensemble models"
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Year of publication
Subject
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Artificial intelligence 3 Künstliche Intelligenz 3 Ensemble models 2 Forecasting model 2 Prognoseverfahren 2 ARIMA 1 Algorithm 1 Algorithmic Investment Strategy 1 Algorithmus 1 Anlageverhalten 1 Anomaly detection 1 Bank Lending 1 Banking 1 Behavioural finance 1 Betrug 1 Business start-up 1 Classification and Regression Trees 1 Credit-Scoring 1 Crunchbase 1 Data security 1 Datensicherheit 1 Deep Learning 1 Economic optimization machine learning outputs 1 Ensemble Models 1 Exit prediction 1 Fraud 1 Funding prediction 1 Hybrid/Ensemble Models 1 IT crime 1 IT-Kriminalität 1 Integration of machine learning and statistical risk modelling 1 LSTM 1 Learning process 1 Lernprozess 1 Machine learning 1 Modelevaluation 1 Neural Networks 1 Neural networks 1 Neuronale Netze 1 Payment fraud risk management 1
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Online availability
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Free 4 CC license 2
Type of publication
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Article 3 Book / Working Paper 1
Type of publication (narrower categories)
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Article in journal 2 Aufsatz in Zeitschrift 2 Arbeitspapier 1 Graue Literatur 1 Non-commercial literature 1 Working Paper 1
Language
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English 3 Undetermined 1
Author
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Aktan, Bora 1 Das, Sanjiv R. 1 Domenig, Thomas 1 Ince, Huseyin 1 Kashif, Kamil 1 Raza, Hussain 1 Ross, Greg 1 Rossi, Sebastiano 1 Sciro, Daniel 1 Vanini, Paolo 1 Zvizdic, Ermin 1 Ślepaczuk, Robert 1
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Published in...
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Financial innovation : FIN 1 Journal of BRSA Banking and Financial Markets 1 The Journal of finance and data science : JFDS 1 Working papers 1
Source
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ECONIS (ZBW) 3 RePEc 1
Showing 1 - 4 of 4
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LSTM-ARIMA as a hybrid approach in algorithmic investment strategies
Kashif, Kamil; Ślepaczuk, Robert - 2024
Persistent link: https://www.econbiz.de/10014634690
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Online payment fraud : from anomaly detection to risk management
Vanini, Paolo; Rossi, Sebastiano; Zvizdic, Ermin; … - In: Financial innovation : FIN 9 (2023) 1, pp. 1-25
Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual's online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms. This is a challenge for machine learning...
Persistent link: https://www.econbiz.de/10014289065
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CapitalVX : a machine learning model for startup selection and exit prediction
Ross, Greg; Das, Sanjiv R.; Sciro, Daniel; Raza, Hussain - In: The Journal of finance and data science : JFDS 7 (2021), pp. 94-114
Using a big data set of venture capital financing and related startup firms from Crunchbase, this paper develops a machine-learning model called CapitalVX (for "Capital Venture eXchange") to predict the outcomes for startups, i.e., whether they will exit successfully through an IPO or...
Persistent link: https://www.econbiz.de/10013162868
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A Comparative Analysis of Individual and Ensemble Credit Scoring Techniques in Evaluating Credit Card Loan Applications
Ince, Huseyin; Aktan, Bora - In: Journal of BRSA Banking and Financial Markets 4 (2010) 1, pp. 74-90
performance of both individual models by using neural networks, and classification and regression trees and ensemble models by … using Bagging and Adaboost techniques. Experimental studies using real world data sets have demonstrated that the ensemble … models outperform the other credit scoring models. …
Persistent link: https://www.econbiz.de/10010611346
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