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VOVK, V., GAMMERMAN, A. & SHAFER, G. (2005). Algorithmic Learning in a Random World. New York: Springer.V. Vovk, A. Gammerman, and G. Shafer. Algorithmic learning in a random world. Springer, Berlin, 2005.Vovk V, Gammerman A, Shafer G. Algorithmic Learning in a Random World. ...
出版年:2005-4-18 页数:324 定价:USD 149.00 装帧:Hardcover ISBN:9780387001524 豆瓣评分 评价人数不足 评价: 写笔记 写书评 加入购书单 分享到 内容简介· ··· Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogoro...
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In Proc. 15th annual conference on Computational Learning Theory (COLT 2002) (eds Kivinen, J. & Sloan, R. H.) 216–228 (Springer, Sydney, 2002). Daley, R. P. Minimal-program complexity of pseudo-recursive and pseudo-random sequences. Math. Syst. Theory 9, 83–94 (1975). Article ...
We will use Hidden Markov Models to produce a volatility regime detection model. This will be used to veto orders in a short-term trend following strategy to increase profitability. Asset Returns Forecasting using Machine Learning We will use numerous machine learning techniques such as Random Forest...
If you are reading this, you are probably aware that machine learning (ML) has become a strategic capability in many industries, including the investment industry. The explosion of digital data closely related to the rise of ML is having a particularly powerful impact on investing, which already...
Intuitively, a GBN makes explicit the possible transitions between the contained models, i.e. the phases, along with the driving variables in these phases. This is not only advantageous from a representational point of view, but since constraints are encoded in the model, parameter learning will...
Secondly, it provides another way of learning the basics of algorithmic problem solving by helping the reader build an intuition for problem solving.The algorithmic approach to solving problems in computer technology is an essential tool. This unique book teaches the fundamental principles of using ...