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A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange
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The article was published on 2014-01-01 and is currently open access. It has received 3 citations till now. The article focuses on the topics: Market maker & Stock market.read more
Citations
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Knowledge Engineering and Intelligent Systems - Chaotic Time Series Prediction Using a Neuro-Fuzzy System with Time-Delay Coordinates
TL;DR: In this article, the authors investigated the use of the delay coordinate embedding technique in the multi-input-multi-output-adaptive-network-based fuzzy inference system (MANFIS) for chaotic time series prediction.
Dissertation
Prediction of Stock Prices Using Technical Analysis in Selected Companies Listed On the Nairobi Securities Exchange
TL;DR: In this article, the authors present a survey of the literature in the area of KnowLEDGEMENT and its application in the field of computer science, focusing on the following topics:
References
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Book
Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Journal ArticleDOI
Data mining and knowledge discovery: making sense out of data
TL;DR: Without a concerted effort to develop knowledge discovery techniques, organizations stand to forfeit much of the value from the data they currently collect and store.
Book
The scientist and engineer's guide to digital signal processing
TL;DR: Getting Started with DSPs 30: Complex Numbers 31: The Complex Fourier Transform 32: The Laplace Transform 33: The z-Transform Chapter 27 Data Compression / JPEG (Transform Compression)
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A theory of learning from different domains
Shai Ben-David,John Blitzer,Koby Crammer,Alex Kulesza,Fernando Pereira,Jennifer Wortman Vaughan +5 more
TL;DR: A classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains and shows how to choose the optimal combination of source and target error as a function of the divergence, the sample sizes of both domains, and the complexity of the hypothesis class.