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• Weka
  ...ules, and visualization. It is also well-suited for developing new machine learning schemes. ...cs.waikato.ac.nz/ml/weka/ Weka ] web site. It was developed by the Machine Learning Group at University of Waikato in New Zealand.
  540 bytes (85 words) - 21:09, 26 September 2012
• Class meeting for 10-605 2012 02 28
  ...h Large Datasets 10-605 in Spring 2012|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2012]]. * Randomized algorithms (Bloom filters and LSH); map-reduce version of PageRank
  850 bytes (118 words) - 15:02, 5 March 2012
• Class meeting for 10-605 2013 02 25
  ...h Large Datasets 10-605 in Spring 2013|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2013]]. * [http://dl.acm.org/citation.cfm?id=1219840.1219917 Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Cluste
  750 bytes (102 words) - 17:15, 8 January 2014
• Class meeting for 10-605 in Fall 2016 Probability Review
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2016]]. * What are streaming machine learning algorithms: ML algorithms that never load in the data
  949 bytes (131 words) - 16:02, 10 August 2017
• Class meeting for 10-605 Probability Review
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2017]]. * What are streaming machine learning algorithms: ML algorithms that never load in the data
  874 bytes (123 words) - 17:46, 30 August 2017
• Class meeting for 10-605 SSL on Graphs
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2017]]. ...mmer (2009):] New regularized algorithms for transductive learning Machine Learning and Knowledge Discovery in Databases, 442-457
  2 KB (214 words) - 12:20, 14 November 2017
• Class meeting for 10-405 Probability Review
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. * What are streaming machine learning algorithms: ML algorithms that never load in the data
  945 bytes (137 words) - 14:27, 24 January 2018
• Class meeting for 10-405 SSL on Graphs
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ...mmer (2009):] New regularized algorithms for transductive learning Machine Learning and Knowledge Discovery in Databases, 442-457
  2 KB (231 words) - 10:50, 30 March 2018
• Class meeting for 10-605 2013 04 01
  ...h Large Datasets 10-605 in Spring 2013|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2013]]. ...p://jmlr.csail.mit.edu/papers/volume10/newman09a/newman09a.pdf Distributed Algorithms for Topic Models], Newman et al, JMLR 2009.
  745 bytes (107 words) - 17:18, 8 January 2014
• Class meeting for 10-605 2012 04 10
  ...h Large Datasets 10-605 in Spring 2012|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2012]]. ...p://jmlr.csail.mit.edu/papers/volume10/newman09a/newman09a.pdf Distributed Algorithms for Topic Models], Newman et al, JMLR 2009.
  808 bytes (119 words) - 17:04, 10 April 2012
• Class meeting for 10-605 Parameter Servers
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]]. ...w.cs.cmu.edu/~feixia/files/ps.pdf Parameter Server for Distributed Machine Learning]
  1 KB (174 words) - 11:37, 28 November 2017
• Winnow Algorithm
  Winnow Algorithm is a [[category::method | ]] for learning a linear classifier/decision hyper-plane from labeled examples. It scales w ...n Irrelevant Attributes Abound: A New Linear-threshold Algorithm", Machine Learning]
  635 bytes (79 words) - 06:09, 6 November 2012
• Class meeting for 10-405 Parameter Servers
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ...w.cs.cmu.edu/~feixia/files/ps.pdf Parameter Server for Distributed Machine Learning]
  1 KB (174 words) - 15:29, 15 January 2018
• Class meeting for 10-605 Randomized
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. * [http://dl.acm.org/citation.cfm?id=1219840.1219917 Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Cluste
  1 KB (223 words) - 16:28, 11 August 2016
• Class Meeting for 10-710 09-29-2011
  ...ds for Structured and Interdependent Output Variables]. Journal of Machine Learning Research 6:1453–1484. ...ith 2011]; also, A.5 (in the appendix) discusses "aggressive" optimization algorithms
  2 KB (261 words) - 19:23, 28 September 2011
• Class meeting for 10-605 SGD for MF 2 and Randomized Algorithms
  ...h Large Datasets 10-605 in Spring 2014|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2014]]. * [http://www.cs.cmu.edu/~wcohen/10-605/randomized-algs.pptx Randomized Algorithms - Slides]
  877 bytes (123 words) - 11:22, 26 February 2014
• Class meeting for 10-605 Graphs 2
  ...h Large Datasets 10-605 in Spring 2014|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2014]]. * [http://www.umiacs.umd.edu/~amit/Papers/goyalPointQueryEMNLP12.pdf Sketch Algorithms for Estimating Point Queries in NLP.] Amit Goyal, Hal Daume III, and Graha
  936 bytes (135 words) - 09:15, 28 April 2014
• Class meeting for 10-605 Scalable PageRank
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. ...p], [http://www.cs.cmu.edu/~wcohen/10-605/randomized-algs-2.pdf Randomized Algorithms PDF version].
  2 KB (321 words) - 13:31, 10 August 2016
• Class meeting for 10-605 2013 02 18
  ...h Large Datasets 10-605 in Spring 2013|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2013]]. * [http://dl.acm.org/citation.cfm?id=1219840.1219917 Randomized Algorithms and NLP: Using Locality Sensitive Hash Functions for High Speed Noun Cluste
  1,010 bytes (140 words) - 17:13, 8 January 2014
• NIPS 1988-1999
  ...cognitive science (CS), control and navigation (CN), implementations (IM), learning theory (LT), neuroscience (NS), signal processing (SP), vision sciences (VS
  501 bytes (71 words) - 16:24, 31 March 2011
• 10-601B Boosting and Other Ensembles
  This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] * [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich
  1,006 bytes (139 words) - 10:18, 12 January 2016
• 10-601 Ensembles 2
  This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]] * [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich
  1 KB (188 words) - 17:36, 21 July 2014
• Class meeting for 10-605 Subsampling a Graph
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. ..., and Kevin Lang. "Local partitioning for directed graphs using PageRank." Algorithms and Models for the Web-Graph. Springer Berlin Heidelberg, 2007. 166-178.]
  2 KB (230 words) - 16:44, 1 August 2017
• 10-601 Ensembles
  This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] * [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich
  1 KB (181 words) - 16:45, 6 January 2016
• Class meeting for 10-605 GraphLab
  ...ith Large Datasets 10-605 in Fall 2015|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall 2015]]. ...tp://www.cs.cmu.edu/~wcohen/postscript/iswc-2010.pdf Signal/Collect: Graph Algorithms for the (Semantic) Web] in ISWC-2010. [http://www.cs.cmu.edu/~wcohen/10-60
  1 KB (176 words) - 18:33, 6 December 2015
• Class meeting for 10-605 Randomized Algorithms
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]]. ...u.edu/~wcohen/10-605/notes/randomized-algs.pdf lecture notes on randomized algorithms].
  3 KB (406 words) - 11:34, 28 November 2017
• Class meeting for 10-405 Randomized Algorithms
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ...u.edu/~wcohen/10-605/notes/randomized-algs.pdf lecture notes on randomized algorithms] (covering Bloom filters and countmin sketches).
  3 KB (431 words) - 10:43, 23 April 2018
• Class meeting for 10-605 Workflows For Hadoop
  ...ith Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]]. ...r Text Categorization]. Proceedings of International Conference on Machine Learning (ICML), 1997.
  3 KB (434 words) - 12:37, 19 September 2017
• Class meeting for 10-405 Workflows For Hadoop
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ...r Text Categorization]. Proceedings of International Conference on Machine Learning (ICML), 1997.
  3 KB (420 words) - 11:12, 5 March 2018
• Class meeting for 10-605 LDA
  ...ith Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]]. ..., and Michael I. Jordan. "Latent Dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.
  2 KB (296 words) - 11:31, 20 November 2017
• Class meeting for 10-405 LDA
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ..., and Michael I. Jordan. "Latent Dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.
  2 KB (315 words) - 10:23, 16 April 2018
• Multi-class Classification
  In machine learning, multiclass or multinomial classification is the problem of classifying ins ...rally permit the use of more than two classes, others are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of
  1 KB (210 words) - 07:56, 2 October 2012
• Class Meeting for 10-710 09-22-2011
  ...g Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, Collins, EMNLP 2002]. ...= A.| last = Globerson| pages = 305–312| title = Exponentiated gradient algorithms for log-linear structured prediction}}]]. A more recent EG-based approach.
  2 KB (291 words) - 16:39, 22 September 2011
• Class Meeting for 10-707 10/6/2010
  ...g Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, Collins, EMNLP 2002]. ...= A.| last = Globerson| pages = 305–312| title = Exponentiated gradient algorithms for log-linear structured prediction}}]]. A more recent EG-based approach.
  2 KB (279 words) - 10:22, 4 October 2010
• Daume et al, ML 2009
  ...gford, J., and Marcu, D. 2009. Search-based structured prediction. Machine Learning. 75. 3. p297-325 ...oduces [[UsesMethod::SEARN]], a meta-algorithm that combines searching and learning to make structured predictions. Note that this is the journal version of th
  1 KB (163 words) - 18:22, 30 September 2010
• Machine Learning with Large Datasets 10-605
  * Prerequisites: a machine learning course (e.g., 10-701 or 10-601) must be taken either before, or concurrentl * Sample syllabus: [[Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012]]
  5 KB (671 words) - 12:55, 14 November 2011
• Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012
  ...taking 10-605 now, you're probably looking for the syllabus for [[Machine Learning with Large Datasets 10-605 in Spring 2013]].''' * Tues Jan 24. [[Class meeting for 10-605 2012 01 24|Streaming algorithms and Naive Bayes.]]
  5 KB (683 words) - 09:48, 28 March 2013
• Class meeting for 10-405 Parallel Perceptrons
  ...h Large Datasets 10-405 in Spring 2018|schedule]] for the course [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. * Parallellizing streaming ML algorithms
  2 KB (308 words) - 11:28, 6 March 2018
• Information Extraction 10-707 in Fall 2010
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI * Prerequisites: a machine learning course (e.g., 10-701 or 10-601) or consent of the instructor.
  4 KB (620 words) - 10:40, 20 September 2010
• 10-601 Clustering
  This a pair of lectures used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]]. * K-means algorithms
  997 bytes (149 words) - 16:49, 6 January 2016
• 10-601 Sequences
  This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] * What the Viterbi and forward-background algorithms are:
  1 KB (181 words) - 15:16, 21 April 2016
• K-means
  ...the data as well as in the iterative refinement approach employed by both algorithms.
  1 KB (190 words) - 00:02, 28 March 2011
• Machine Learning with Large Datasets 10-605 in Spring 2013
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI ** a machine learning course (e.g., 10-701 or 10-601). You may take this concurrently with the i
  5 KB (715 words) - 12:07, 26 April 2013
• Project dong, 10-802 spring 2010
  The following is an overview of techniques using '''machine learning techniques''' ..._classifier_learning | Naive Bayes]] & [[Support vector machine classifier learning | SVM]]
  3 KB (397 words) - 17:01, 1 February 2011
• The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity
  ...ch they used 2000 papers categorized into one of the sub fields of Machine learning. * '''PHITS''' - D. Cohn and H. Chang. Learning to probabilistically identify authoritative documents. In
  4 KB (610 words) - 17:08, 5 November 2012
• Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs
  title={Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs}, journal={Machine Learning and Knowledge Discovery in Databases},
  3 KB (495 words) - 13:26, 5 November 2012
• Machine Learning with Large Datasets 10-605 in Spring 2012
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI ** a machine learning course (e.g., 10-701 or 10-601). You may take this concurrently with the i
  5 KB (716 words) - 11:34, 1 May 2012
• Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016
  This is the syllabus for [[Machine Learning with Large Datasets 10-605 in Fall 2016]]. ...lable classification, Scalable Rocchio and TFIDF, Abstracts for map-reduce algorithms, Joins in Hadoop, TFIDF in Pig, Guinea Pig intro, TFIDF in Guinea Pig
  7 KB (1,002 words) - 11:54, 11 August 2017
• 10-601 Wrap-up on Linear Classification
  This a lecture used in the [[Syllabus for Machine Learning 10-601]] To summarize this part of the course, we looked at a four algorithms in some detail.
  1 KB (225 words) - 16:52, 30 July 2013
• Class Meeting for 10-710 10-27-2011
  * [http://aclweb.org/anthology-new/J/J98/J98-4003.pdf Machine Transliteration], K. Knight and J. Graehl, CL 1998 ...rs on WFST algorithms are indexed [http://www.cs.nyu.edu/~mohri/transducer-algorithms.html here]
  2 KB (279 words) - 16:31, 27 October 2011
• Globerson et al. ICML 2007. Exponentiated Gradient Algorithms for Log Linear Structured Prediction
  ...st = A.| last = Globerson| pages = 305-312| title = Exponentiated gradient algorithms for log-linear structured prediction| url = http://people.csail.mit.edu/mco ...log likelihood of a CRF is often via [[conjugate-gradient]] or [[L-BFGS]] algorithms ([[RelatedPaper::Sha_2003_shallow_parsing_with_conditional_random_fields |
  5 KB (739 words) - 02:59, 27 September 2011
• Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013
  This is the syllabus for [[Machine Learning with Large Datasets 10-605 in Spring 2013]]. * Wed Jan 23. [[Class meeting for 10-605 2013 01 23|Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large
  7 KB (1,005 words) - 17:20, 10 January 2014
• Syllabus for Machine Learning with Large Datasets 10-405 in Spring 2018
  This is the syllabus for [[Machine Learning with Large Datasets 10-405 in Spring 2018]]. ...d on [https://www.umiacs.umd.edu/~hal/docs/daume07easyadapt.pdf a transfer learning method] which works similarly. Many wikipedia pages are available in multip
  9 KB (1,249 words) - 13:46, 30 April 2018
• Daume and Marcu 2005 Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction
  ...ediction. In ''Proceedings of the 22nd international conference on Machine learning'' (ICML '05). ACM, New York, NY, USA, 169-176. The authors present the Learning as Search Optimization (LaSO) framework for structured prediction. The alg
  3 KB (468 words) - 03:08, 11 October 2011
• Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2014
  This is the syllabus for [[Machine Learning with Large Datasets 10-605 in Spring 2014]]. * Wed Jan 22. [[Class meeting for 10-605 Streaming Naive Bayes|Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large
  6 KB (839 words) - 17:09, 2 June 2014
• Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2015
  This is the syllabus for [[Machine Learning with Large Datasets 10-605 in Fall 2015]]. * Tues Sep 8. [[Class meeting for 10-605 Streaming Naive Bayes|Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large
  6 KB (908 words) - 10:07, 11 October 2016
• Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017
  This is the syllabus for [[Machine Learning with Large Datasets 10-605 in Fall 2017]]. ...rkflows For Hadoop 1]]. Scalable classification, Abstracts for map-reduce algorithms, Joins in Hadoop
  9 KB (1,220 words) - 12:06, 28 November 2017
• Class Meeting for 10-710 10-11-2011
  ...w, in semiring weighted logic program notation, the CKY and Earley parsing algorithms * [http://aclweb.org/anthology-new/P/P06/P06-1055.pdf Learning Accurate, Compact, and Interpretable Tree Annotation], S. Petrov, L. Barret
  3 KB (473 words) - 17:59, 9 October 2011
• Hunt for Project partners
  *project:[[Active Learning in Link Prediction for social networks]] ...f friend suggestions provided by the system, which is similar to an active learning framework that the system can query the user by providing a suggestion and
  3 KB (423 words) - 16:54, 12 October 2012
• Berg-Kirkpatrick et al, ACL 2010: Painless Unsupervised Learning with Features
  ...atrick, A. Bouchard-Côté, J. DeNero and D. Klein. '''Painless Unsupervised Learning with Features''', ''Human Language Technologies: The 2010 Annual Conference ...nal probability distributions (CPDs) with miniature log-linear models. Two algorithms for unsupervised training of featurized HMMs are proposed.
  5 KB (590 words) - 14:37, 18 September 2011
• Ristad and Yianilos 1997 Learning String Edit Distance
  .... Learning string-edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20: 522--532, 1998. ...as an efficient [[UsesMethod:: Expectation Maximization | EM]] variant for learning the edit costs.
  4 KB (638 words) - 09:36, 2 November 2011
• Dwijaya Social Media Analysis
  ...-LICO project], specifically in the building of an ontology of data mining algorithms and models. I have also worked previously on the area of opinion mining and
  3 KB (426 words) - 12:40, 8 September 2011
• Within Document Coreference (WDC)
  ...latedPaper::Culotta et al.,2007]]). Many researchers have explored machine learning approaches by treating the problem as a pair-wise binary classification pro Learning-Based Java (LBJ) co-reference package. The BART system is from the Johns Ho
  4 KB (543 words) - 21:56, 25 September 2011
• DmitryDavidov et al. CoNLL
  ...o automatically generate patterns, and feed those patterns to some machine learning algorithm to detect sarcasm. However, from this paper, I have no idea how t 3. It didn't consider any baseline algorithms. For example, they can compare their method to other semi-supervised method
  4 KB (687 words) - 15:34, 2 October 2012
• Margin Infused Relaxed Algorithm
  ...oseph Keshet, Shai Shalev-Shwartz, Yoram Singer. Online Passive-Aggressive Algorithms. JMLR 7(Mar):551--585, 2006. | Crammer et al., 2006]]. A general definition of online training algorithms can be written down as follows:
  6 KB (926 words) - 17:44, 29 November 2011
• Detection of Ad Hominem attacks in blog and review data
  Use machine-learning and/or probabilistic topic modeling to detect examples of personal insult i ...ent aimed at persons, not ideas). In addition, I would like to try machine learning based on more advanced features, such as part-of-speech tags and inferred t
  5 KB (700 words) - 16:39, 3 November 2012
• Machine Learning with Large Datasets 10-405 in Spring 2018
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI ** a CMU intro machine learning course (e.g., 10-701, 10-715, 10-601, 10-401).
  14 KB (2,139 words) - 15:18, 4 May 2018
• J. Artiles et al. EMNLP 2009
  regardless of the underlying machine learning algorithms used and parameter settings.
  2 KB (344 words) - 05:47, 23 November 2010
• Hopkins and May, EMNLP 2011. Tuning as Ranking
  ...[[Category::paper]] presents a simple and scalable method for statistical machine translation parameter tuning based on the pairwise approach to ranking. Th In a pairwise ranking approach, the learning task is framed as the classification of candidate pairs into two categories
  8 KB (1,132 words) - 20:40, 29 November 2011
• Machine Learning with Large Datasets 10-605 in Fall 2017
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI ** a CMU intro machine learning course (e.g., 10-701, 10-715, 10-601, 10-401).
  18 KB (2,764 words) - 12:30, 5 November 2017
• Bootstrapping
  '''Bootstrapping''' is a term used to define a general class of algorithms which benefit from a small set of labeled examples and a large amount of un By far the largest cost in applied machine learning, in terms of human labor, is annotation of data. In order to train supervis
  4 KB (667 words) - 02:13, 30 November 2011
• Support Vector Machines
  This is a machine learning [[Category::Method | model]]. There exist several specialized algorithms for quickly solving the Quadratic Programming problem that arises from SVMs
  3 KB (450 words) - 23:25, 31 March 2011
• PCFGs
  ...including: grammar checking, lexical learning and [[Machine_Translation | machine translation]]. ...ley_algorithm Earley] and [http://en.wikipedia.org/wiki/CYK_algorithm CYK] algorithms.
  3 KB (531 words) - 16:20, 29 November 2011
• Smith and Eisner 2008:Dependency parsing by belief propagation
  ...tuent Parsing]]. The paper formulates the dependency parsing problem as a learning and decoding problem on a graphical model with global constraints. The auth ...mployed global factors in this work. So, the trick here is to run backward algorithms from marginal distributions (this is very similar to the forward-backward a
  8 KB (1,193 words) - 17:15, 13 October 2011
• M. Hurst and K. Nigam. Retrieving topical sentiments from online document collection.
  ...gue for the fusion of Topicality and Polarity by using statistical machine learning approaches to identify topics and shallow NLP techniques to determine polar ...Algorithm | Winnow Classifier]] which is an online learning algorithm for learning a linear decision boundary. Since they dont have enough sentence level anno
  6 KB (882 words) - 08:38, 6 November 2012
• Machine Learning with Large Datasets 10-605 in Fall 2016
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI ** a CMU intro machine learning course (e.g., 10-701, 10-715 or 10-601).
  18 KB (2,768 words) - 10:09, 7 April 2017
• Sparse Additive Generative Models of Text
  This paper evaluates numerous different algorithms on a variety of datasets. They use the classic benchmark "Twenty Newsgroups ...sesProblem::Lack of Sparsity]]. Overfitting is always a problem in Machine Learning and SAGE attempts to deal with the issue by imposing sparsity. They use an
  5 KB (795 words) - 17:53, 5 October 2012
• Structured Prediction 10-710 in Fall 2011
  ...en William Cohen] and [http://www.cs.cmu.edu/~nasmith Noah Smith], Machine Learning Dept and LTI * Prerequisites: a machine learning course (e.g., 10-701 or 10-601) or consent of the instructor.
  7 KB (1,020 words) - 14:11, 30 November 2011
• Yoshikawa 2009 jointly identifying temporal relations with markov logic
  ...f relations: event-event, event-time, event-DCT, compared to other machine learning based approaches. ...-OR-OVERLAP'', ''OVERLAP-OR-AFTER'', and ''VAGUE''. Training and inference algorithms are provided by [http://code.google.com/p/thebeast/ Markov thebeast], a Mar
  7 KB (1,003 words) - 22:31, 29 September 2011
• Forum-Based Language Learning Analysis
  ...of time and effort. Fortunately, some tools can be used to facilitate the learning task. Online forums are a type of social medium used by learners, for exam ...a forum dedicating to studying the Spanish language to facilitate language learning by identify salient topics.
  6 KB (834 words) - 10:46, 15 February 2011
• Machine Learning with Large Datasets 10-605 in Fall 2015
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI ** a CMU intro machine learning course (e.g., 10-701, 10-715 or 10-601).
  14 KB (2,147 words) - 12:29, 20 September 2015
• Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2015
  This is the syllabus for [[Machine Learning with Large Datasets 10-605 in Spring 2015]]. * Tues Jan 20. [[Class meeting for 10-605 Streaming Naive Bayes|Streaming algorithms and Naive Bayes; The stream-and-sort design pattern; Naive Bayes for large
  9 KB (1,328 words) - 14:50, 14 October 2015
• L. Ku, Y. Liang, and H. Chen. Opinion extraction, summarization and tracking in news and blog corpora. In Proceedings of AAAI-2006
  selected as the experimental materials. Algorithms for The method is better than the machine learning algorithm(SVM,C5 decision tree). Because semantics within a word is not eno
  4 KB (534 words) - 18:44, 26 October 2012
• Lin and Wu. 2009. Phrase Clustering for Discriminative Learning.
  ...2009| first = D.| last = Lin| title = Phrase clustering for discriminative learning| url = http://www.aclweb.org/anthology/P/P09/P09-1116.pdf }} ...this is a very important consideration. It took about 20 minutes on a 1000 machine cluster to perform K-means to convergence on their dataset. The "similarity
  4 KB (577 words) - 01:07, 30 September 2011
• Machine Learning with Large Datasets 10-605 in Spring 2015
  * Instructor: [http://www.cs.cmu.edu/~wcohen William Cohen], Machine Learning Dept and LTI ** a machine learning course (e.g., 10-701 or 10-601). You may take this concurrently with the i
  15 KB (2,287 words) - 01:09, 7 April 2015
• ToWikify
  ...supervised_learning_algorithm_for_link_prediction]] || [[Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs]] [http:// ...ent_Variable_Model_for_SMT]] || [[An End-to-End Discriminative Approach to Machine Translation]] [http://www.seas.upenn.edu/~taskar/pubs/acl06.pdf] ||lingwang
  12 KB (1,642 words) - 17:02, 30 November 2012
• Recent or influential technical papers in Analysis of Social Media
  ...influences. In Proceedings of the 24th international conference on Machine learning, 233–240. * Kearns, M. B.M. Local Algorithms for Finding Interesting Individuals in Large Networks.
  11 KB (1,501 words) - 21:42, 29 September 2012
• Daume ICML 2009
  ...rsing|dependency parsing]], as well as briefly touching on semi-supervised learning with [[UsesMethod::SEARN|SEARN]]. ...ner models), it converges significantly more quickly than any of the other algorithms. The results for the supervised methods cannot be fairly compared, as [[Use
  10 KB (1,486 words) - 11:54, 30 September 2011
• Expectation–maximization algorithm
  ...[[k-means algorithm|''k''-means algorithm]] is an example of this class of algorithms. ...last3=Friedman|first3=Jerome |year=2001 |title=The Elements of Statistical Learning |isbn=0-387-95284-5 |publisher=Springer |location=New York |chapter=8.5 The
  39 KB (5,817 words) - 21:17, 26 September 2012
• Roth and Yih, ICML 2005. Integer Linear Programming Inference for Conditional Random Fields
  ...ceedings of the <math> 22^{nd} </math> International Conference on Machine learning'', ICML'05, New York, NY, USA. ...ove the results with statistically significant gains for both CRF training algorithms.
  7 KB (1,134 words) - 01:32, 1 November 2011