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• W3044400804 abstract "The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM." @default.
• W3044400804 created "2020-07-29" @default.
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• W3044400804 date "2020-01-01" @default.
• W3044400804 modified "2023-09-24" @default.
• W3044400804 title "Application of Machine Learning Algorithms in Stock Market Prediction" @default.
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• W3044400804 doi "https://doi.org/10.4018/978-1-7998-3645-2.ch007" @default.
• W3044400804 hasPublicationYear "2020" @default.
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