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Stochastic optimization is playing an increasingly important in machine learning in the big data era. In this paper, we use forward-backward splitting for the stochastic optimization problems, where the objective is the sum of two functions: one is the expected risk function, another is a regularized term. At each iteration of this method, we just use a single sample to adjust the variables. We prove...
This paper presents a machine learning method for event-driven stock prediction, using L1 regularized Logistic regression model. It studies the stock price movement after listed companies make announcements. The model uses specific events extracted from these announcements and combine with financial indicators of listed companies, macro indicators, and technical indicators as dependent variables....
This paper is motivated by major needs for fast and accurate on-line data analysis tools in the emerging electric energy systems, due to the recent penetration of distributed green energy, distributed intelligence, and plug-in electric vehicles. Instead of taking the traditional complex physical model based approach, this paper proposes a data-driven method, leading to an effective topology estimation...
Online shopping mall is emerging as a main marketing channel. The Web-based decision support system (DSS) in Web 2.0 is constructed to facilitate customers to share profiles and comments. Recommendation system is designed to cluster the customers and products respectively, and produce some association rules between customers and products. These have enabled the system to trigger consumer buying behavior...
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