A general approach for predicting the behavior of the Supreme Court of the United States
暂无分享,去创建一个
[1] Jeffrey A. Segal,et al. The Supreme Court and the Attitudinal Model Revisited , 1993 .
[2] Lee Epstein,et al. Ideological Values and the Votes of U.S. Supreme Court Justices Revisited , 1989, The Journal of Politics.
[3] Jeffrey A. Segal,et al. The Influence of Stare Decisis on the Votes of United States Supreme Court Justices , 1996 .
[4] J. Segal. Separation-of-Powers Games in the Positive Theory of Congress and Courts , 1997, American Political Science Review.
[5] Gregory A. Caldeira,et al. Of time and consensual norms in the Supreme Court , 1998 .
[6] Andrew D. Martin,et al. Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999 , 2002, Political Analysis.
[7] Andrew D. Martin,et al. The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decisionmaking , 2004 .
[8] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[9] Andrew D. Martin,et al. Competing Approaches to Predicting Supreme Court Decision Making , 2004, Perspectives on Politics.
[10] Rich Caruana,et al. An empirical comparison of supervised learning algorithms , 2006, ICML.
[11] Andrew D. Martin,et al. Ideological Drift Among Supreme Court Justices: Who, When, and How Important? , 2007 .
[12] E. A. Leicht,et al. Large-scale structure of time evolving citation networks , 2007, 0706.0015.
[13] Andrew D. Martin,et al. Assessing Preference Change on the US Supreme Court , 2007 .
[14] Michael A. Bailey,et al. Does Legal Doctrine Matter? Unpacking Law and Policy Preferences on the U.S. Supreme Court , 2008, American Political Science Review.
[15] C. Shapiro. Coding Complexity: Bringing Law to the Empirical Analysis of the Supreme Court , 2008 .
[16] Kevin D. Ashley,et al. Automatically classifying case texts and predicting outcomes , 2009, Artificial Intelligence and Law.
[17] Daniel E. Ho,et al. Did a Switch in Time Save Nine , 2010 .
[18] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[19] Patrick C. Wohlfarth,et al. How Public Opinion Constrains the U.S. Supreme Court , 2011 .
[20] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[21] R. Guimerà,et al. Justice Blocks and Predictability of U.S. Supreme Court Votes , 2011, PloS one.
[22] Josh Blackman,et al. FantasySCOTUS: Crowdsourcing a Prediction Market for the Supreme Court , 2011 .
[23] Sean J. Griffith,et al. Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits , 2012 .
[24] B. Desmarais,et al. Standing the Test of Time: The Breadth of Majority Coalitions and the Fate of U.S. Supreme Court Precedents , 2012 .
[25] D. Katz. Quantitative Legal Prediction – or – How I Learned to Stop Worrying and Start Preparing for the Data Driven Future of the Legal Services Industry , 2012 .
[26] Shai Shalev-Shwartz,et al. Online Learning and Online Convex Optimization , 2012, Found. Trends Mach. Learn..
[27] Eric L. Talley,et al. The Measure of a MAC: A Machine-Learning Protocol for Analyzing Force Majeure Clauses in M&A Agreements , 2012 .
[28] Tam Harbert. The Law Machine , 2013, IEEE Spectrum.
[29] Harry Surden,et al. Machine Learning and Law , 2014 .
[30] Sarath Sanga. Choice of Law: An Empirical Analysis , 2014 .
[31] Josh Blackman,et al. Predicting the Behavior of the Supreme Court of the United States: A General Approach , 2014, ArXiv.
[32] Maura R. Grossman,et al. Evaluation of machine-learning protocols for technology-assisted review in electronic discovery , 2014, SIGIR.
[33] Gilles Louppe,et al. Understanding Random Forests: From Theory to Practice , 2014, 1407.7502.
[34] William Bialek,et al. Statistical Mechanics of the US Supreme Court , 2013, Journal of Statistical Physics.
[35] D. Katz,et al. Law on the Market? Evaluating the Securities Market Impact of Supreme Court Decisions∗ , 2015 .
[36] Heike Freud,et al. On Line Learning In Neural Networks , 2016 .
[37] Nikolaos Aletras,et al. Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective , 2016, PeerJ Comput. Sci..
[38] D. Katz,et al. Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making , 2015, 1508.05751.