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Online Learning and Its Implementation in Intelligent Traffic and Finantial Market

Author: ZhouZuo
Tutor: ZhangJunPing
School: Fudan University
Course: Computer Software and Theory
Keywords: Machine Learning Online Learning Perceptron Precise Parking Portfolio regret
CLC: U495;F830
Type: Master's thesis
Year: 2011
Downloads: 90
Quote: 1
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Abstract


Online learning is one of the hot research direction in recent years, the field of machine learning , increasingly widespread attention because of its highly consistent with a variety of problems in the real world . Have a natural advantage for new information to solve today 's social life constantly explosive growth and the continuous accumulation of old information that redundant processing . When the research on the topic of online learning is still at the theoretical level , the main concern is how solving and algorithms from online convex optimization perspective theoretical error bounds . The selected highly value a few questions online learning , including : 1 ) online algorithm nuclear methods and how to avoid the the growing nuclear matrix ; 2) regression algorithm is Online Boosting the performance advantages of the regression technique , and train accurate parking problems ; 3 ) how to take advantage of online algorithm design online portfolio strategy , how to use the characteristics of the fluctuations of the market itself to improve the online portfolio strategy performance . We propose the following improved algorithms and assess their performance : ( ?) Investigated several kernel-based Perceptron -like algorithm , and use of support vector machine soft margin improved a better performance Projectron algorithm , improved the SMProj algorithm to improve performance while also reducing the size of the support vector set . Good guide for the classification problems in real life . ( ? ) For the simplified model train parking investigated of several regression algorithm performance on the model , and GP Boosting algorithm proposed online method . Through the test confirmed the feasibility and advantages of the of Online Boosting regression algorithm in the train on the parking problem . (?) In the Anticor algorithm inspired , in-depth study proved that the mechanism of the algorithm in the portfolio , at the same time proposed algorithm doubt the improved CorrReg algorithm . Not only through the mutual relations of the market volatility and asset predict the direction of the flow of funds , and the use of internal regret online algorithms to quantify the number of the flow of capital . Making algorithm richer interpretability .

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