Prediction in financial markets: The case for small disjuncts
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[1] G. Soros. The Alchemy of Finance , 1994 .
[2] William N. Goetzmann,et al. Active Portfolio Management , 1999 .
[3] Vladimir Cherkassky,et al. The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.
[4] Tomaso A. Poggio,et al. Extensions of a Theory of Networks for Approximation and Learning , 1990, NIPS.
[5] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[6] James T. Wassell,et al. Bootstrap Methods: A Practitioner's Guide , 2001, Technometrics.
[7] J. R. Quinlan. Improved estimates for the accuracy of small disjuncts , 1991 .
[8] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[9] Simon Kasif,et al. A System for Induction of Oblique Decision Trees , 1994, J. Artif. Intell. Res..
[10] Chandrika Kamath,et al. Combining evolutionary algorithms with oblique decision trees to detect bent-double galaxies , 2000, SPIE Optics + Photonics.
[11] S. Achelis. Technical analysis a to z , 1994 .
[12] Richard J. Bauer,et al. Genetic Algorithms and Investment Strategies , 1994 .
[13] Haym Hirsh,et al. A Quantitative Study of Small Disjuncts , 2000, AAAI/IAAI.
[14] P. Kaufman. New Trading Systems and Methods , 2005 .
[15] Josef Lakonishok,et al. Fundamentals and Stock Returns in Japan , 1991 .
[16] Michael Kearns,et al. Reinforcement learning for optimized trade execution , 2006, ICML.
[17] Foster J. Provost,et al. Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network , 1993, ICML.
[18] Michael R. Chernick,et al. Bootstrap Methods: A Practitioner's Guide , 1999 .
[19] Matthew Saffell,et al. Learning to trade via direct reinforcement , 2001, IEEE Trans. Neural Networks.
[20] A. Shleifer,et al. Inefficient Markets: An Introduction to Behavioral Finance , 2002 .
[21] V. Rich. Personal communication , 1989, Nature.
[22] Mario Bunge,et al. The Myth of Simplicity: Problems of Scientific Philosophy. , 1964 .
[23] E. Fama,et al. Common risk factors in the returns on stocks and bonds , 1993 .
[24] A. Tversky,et al. Judgment under Uncertainty: Heuristics and Biases , 1974, Science.
[25] Donald B. Keim. SIZE-RELATED ANOMALIES AND STOCK RETURN SEASONALITY Further Empirical Evidence , 1983 .
[26] Ronald J. Lanstein,et al. Persuasive evidence of market inefficiency , 1985 .
[27] Sean R Eddy,et al. What is dynamic programming? , 2004, Nature Biotechnology.
[28] Haym Hirsh,et al. The Problem with Noise and Small Disjuncts , 1998, ICML.
[29] Herbert K. H. Lee,et al. Bayesian nonparametrics via neural networks , 2004, ASA-SIAM series on statistics and applied probability.
[30] Stephen José Hanson,et al. Computational Learning Theory and Natural Learning , 1996 .
[31] Geoffrey I. Webb. Further Experimental Evidence against the Utility of Occam's Razor , 1996, J. Artif. Intell. Res..
[32] Sanjeev Arora,et al. Computational Complexity: A Modern Approach , 2009 .
[33] Aswath Damodaran,et al. Strategic Risk Taking: A Framework for Risk Management , 2007 .
[34] Brett Presnell,et al. Bootstrap Methods: A Practitioner's Guide , 2002 .
[35] R. Ball,et al. THE THEORY OF STOCK MARKET EFFICIENCY: ACCOMPLISHMENTS AND LIMITATIONS , 1995 .
[36] H. Akaike. A new look at the statistical model identification , 1974 .
[37] John Moody,et al. Developments in forecast combination and portfolio choice , 2001 .
[38] Gary M. Weiss. Learning with Rare Cases and Small Disjuncts , 1995, ICML.
[39] Narasimhan Jegadeesh,et al. Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency , 1993 .
[40] Vittorio Castelli,et al. Bayesian Nonparametrics via Neural Networks , 2005 .
[41] Luís Torgo,et al. A Study on End-Cut Preference in Least Squares Regression Trees , 2001, EPIA.
[42] J. Ross Quinlan. Improved Estimates for the Accuracy of Small Disjuncts , 2005, Machine Learning.
[43] Jack D. Schwager. The New Market Wizards: Conversations with America's Top Traders , 1992 .
[44] Jeffrey S. Simonoff,et al. Tree Induction Vs Logistic Regression: A Learning Curve Analysis , 2001, J. Mach. Learn. Res..
[45] Andreas Buja,et al. Data mining criteria for tree-based regression and classification , 2001, KDD '01.
[46] John C. Alexander. Earnings Surprise, Market Efficiency, and Expectations , 1992 .
[47] A. Tversky,et al. Judgment under Uncertainty , 1982 .
[48] Robert C. Holte,et al. Concept Learning and the Problem of Small Disjuncts , 1989, IJCAI.
[49] R. Banz,et al. The relationship between return and market value of common stocks , 1981 .
[50] M. J. Klass,et al. On the Estimation of Security Price Volatilities from Historical Data , 1980 .
[51] Michael J. Pazzani,et al. 10 Reducing the Small Disjuncts Problem by Learning Probabilistic Concept Descriptions , 1998 .