Search results

Create the page "Estimation theory" on this wiki! See also the search results found.

• Class Meeting for 10-710 11-10-2011
  ...lr.csail.mit.edu/papers/volume5/cortes04a/cortes04a.pdf Rational Kernels: Theory and Algorithms], Cortes, Haffner, and Mohri, 2004 * [http://books.nips.cc/papers/files/nips15/AA50.pdf Kernel Dependency Estimation], Weston, Chapelle, Elisseeff, Schoelkopf, and Vapnik, 2003
  1 KB (168 words) - 19:35, 9 November 2011
• Belief Propagation
  ...labeled Data]] (This is a very different estimation approach for parameter estimation in log-linear models.) For lots of really interesting theory about loopy belief propagation, check out Wainwright and Jordan 2008, http:
  6 KB (1,044 words) - 16:16, 13 October 2011
• L-BFGS
  {{MyCiteconference | booktitle = Journal of Optimization Theory and Applications | coauthors = | date = 1985| first = D.F.| last = Shanno | ...og-Linear_Models_on_Unlabeled_Data | Smith & Eisner, ACL 2005: Contrastive Estimation: Training Log-Linear Models on Unlabeled Data ]]
  5 KB (788 words) - 16:55, 31 October 2011
• Expectation–maximization algorithm
  |title=Maximum likelihood theory for incomplete data from an exponential family Rolf Sundberg. 1971. ''Maximum likelihood theory and applications for distributions generated when observing a function of a
  39 KB (5,817 words) - 20:17, 26 September 2012
• Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016
  ...iew|Overview]]. Grading policies and etc, History of Big Data, Complexity theory and cost of important operations ...robability Review|Probability Review]]. Counting for big data and density estimation, streaming Naive Bayes, Rocchio and TFIDF
  7 KB (1,002 words) - 10:54, 11 August 2017
• Machine Learning 10-601 in Fall 2013
  ...on trees, neural networks, computational learning theory, active learning, estimation & the bias-variance tradeoff, hypothesis testing, Bayesian learning, Naïve * Additionally, a probability course is a co-requisite: 36-217: Probability Theory and Random Processes OR 36-225: Introduction to Probability and Statistics
  9 KB (1,409 words) - 16:24, 6 January 2016
• Machine Learning 10-601 in Spring 2016
  ...on trees, neural networks, computational learning theory, active learning, estimation & the bias-variance tradeoff, hypothesis testing, Bayesian learning, Naïve * Additionally, a probability course is a co-requisite: 36-217: Probability Theory and Random Processes OR 36-225: Introduction to Probability and Statistics
  11 KB (1,783 words) - 20:13, 5 September 2016
• Machine Learning 10-601 in Fall 2014
  ...on trees, neural networks, computational learning theory, active learning, estimation & the bias-variance tradeoff, hypothesis testing, Bayesian learning, Naïve * Additionally, a probability course is a co-requisite: 36-217: Probability Theory and Random Processes OR 36-225: Introduction to Probability and Statistics
  11 KB (1,700 words) - 19:45, 18 November 2014
• Stylistic Structure in Historic Legal Text
  ===The Theory=== ...t also consider other optimization methods, such as conditional likelihood estimation, sum of conditional likelihood, and cost-sensitive
  19 KB (3,063 words) - 18:54, 5 December 2011
• Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017
  ...iew|Overview]]. Grading policies and etc, History of Big Data, Complexity theory and cost of important operations ...robability Review|Probability Review]]. Counting for big data and density estimation, streaming Naive Bayes, Rocchio and TFIDF
  9 KB (1,220 words) - 11:06, 28 November 2017
• Syllabus for Machine Learning with Large Datasets 10-405 in Spring 2018
  ...iew|Overview]]. Grading policies and etc, History of Big Data, Complexity theory and cost of important operations ...robability Review|Probability Review]]. Counting for big data and density estimation, streaming Naive Bayes, Rocchio and TFIDF
  9 KB (1,249 words) - 12:46, 30 April 2018