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- 10-601B Generalization and Overfitting: Sample Complexity Results for Supervised Classification 3 → 10-601B Model Selection
- 10-601B Intro to neural Networks → 10-601B Intro to Neural Networks
- 10-601B Perceptrons and SVMs → 10-601B Perceptrons and Large Margin
- 10-601 K-NN And Trees → 10-601 K-NN And Trees - Lecture from Fall 2013
- 10-605 in Spring 2014 → Machine Learning with Large Datasets 10-605 in Spring 2014
- 10-605 in Spring 2015 → Machine Learning with Large Datasets 10-605 in Spring 2015
- A high-performance semi-supervised learning method for text chunking → Ando & Zhang, 2005, A high-performance semi-supervised learning method for text chunking
- Also - a draft schedule for 10-601B → Draft schedule for 10-601B in Spring 2016
- AlternatingMinimization → Alternating Minimization
- Analysis of Social Media Spring, 2011 → User:Manajs/Analysis of Social Media Spring, 2011
- Ando & Zhang, 2005, A high-performance semi-supervised learning method for text chunking → R. K. Ando and T. Zhang. ACL 2005
- Ando and Zhang ACL 2005 → A high-performance semi-supervised learning method for text chunking
- Attribute extraction → Attribute Extraction
- Autolab guide for ta → Autolab Guide for TA
- Automatically Refining the Wikipedia Infobox Ontology → Wu and Weld ACM 2008
- Baum-Welch → Forward-Backward
- BeliefPropagation → Belief Propagation
- Berg-Kirkpatrick, ACL 2010: Painless Unsupervised Learning with Features → Berg-Kirkpatrick et al, ACL 2010: Painless Unsupervised Learning with Features
- Berg-Kirkpatrick, HLT 2010: Painless Unsupervised Learning with Features → Berg-Kirkpatrick, ACL 2010: Painless Unsupervised Learning with Features
- CRF → Conditional Random Fields
- Class Meeting for 10-710 9-27-2011 → Class Meeting for 10-710 09-27-2011
- Class Meeting for 10-802 12/1/2012 → Class Meeting for 10-802 11/29/2012
- Class meeting for 10-605 2013 04 22 → Class meeting for 10-605 2013 04 17
- Class meeting for 10-605 Hadoop 1 → Class meeting for 10-605 More on Stream and Sort
- Class meeting for 10-605 LDA 1 → Class meeting for 10-605 LDA
- Class meeting for 10-605 More on Stream and Sort → Class meeting for 10-605 Phrases with Stream and Sort
- Class meeting for 10-605 PIG → Class meeting for 10-605 Hadoop Overview
- Class meeting for 10-605 Phase Finding → Class meeting for 10-605 Phrase Finding
- Class meeting for 10-605 Randomized Algorithms 1 → Class meeting for 10-605 Randomized Algorithms
- Class meeting for 10-605 Rocchio and On-line Learning → Class meeting for 10-605 Rocchio and Hadoop Workflows
- Class meeting for 10-605 Subsample A Graph → Class meeting for 10-605 Scalable PageRank
- Class meeting for 10-605 Workflows For Hadoop 1 → Class meeting for 10-605 Workflows For Hadoop
- CoNLL '00 → CoNLL'00
- Conditional Random fields → Conditional Random Fields
- Daume et al 2006 → Turney, 2002
- Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Kadie; in JMLR, 1(Oct):49-75, 2000. → Dependency Networks for Inference, Collaborative Filtering, and Data Visualization
- Dependency parsing → Dependency Parsing
- Divergence of probability distributions → Inside Outside algorithm
- EM → Expectation Maximization
- EntropicRegularization → EntropicGraphRegularization
- Finkel 2009 nested named entity recognition → Finkel and Manning, EMNLP 2009. Nested Named Entity Recognition
- Finkel and Manning EMNLP 2009 → Finkel and Manning, EMNLP 2009. Nested Named Entity Recognition
- Forward-backward → Forward-Backward
- Globerson 2007 exponentiated gradient algorithms for log linear structured prediction → Globerson et al. ICML 2007. Exponentiated Gradient Algorithms for Log Linear Structured Prediction
- Globerson et al. 2007 Exponentiated Gradient Algorithms for Log Linear Structured Prediction → Globerson et al. ICML 2007 Exponentiated Gradient Algorithms for Log Linear Structured Prediction
- Globerson et al. ICML 2007 Exponentiated Gradient Algorithms for Log Linear Structured Prediction → Globerson et al. ICML 2007. Exponentiated Gradient Algorithms for Log Linear Structured Prediction
- Globerson et al ICML 2007 → Globerson et al. ICML 2007. Exponentiated Gradient Algorithms for Log Linear Structured Prediction
- Hadoop cluster information → GHC Hadoop cluster information
- Hidden Markov models → Hidden Markov Model
- Huang et al, ACL 2010: Enhancing Cross Document Coreference of Web Documents with Context Similarity and Very Large Scale Text Categorization → Huang et al, Coling 2010: Enhancing Cross Document Coreference of Web Documents with Context Similarity and Very Large Scale Text Categorization
- Huang et al, ACL 2010: Profile Based Cross-Document Coreference Using Kernelized Fuzzy Relational Clustering → Huang et al, ACL 2009: Profile Based Cross-Document Coreference Using Kernelized Fuzzy Relational Clustering
- Information Extraction to Predict Decisions → Information Extraction to Predict Judgement
- Junyang Ng → User:Junyangn
- Jure et al, KDD 2009 → Jure kdd09
- Kelvin Law → User:Cheuktol
- Lafferty 2001 conditional random fields probabilistic models for segmenting and labeling sequence data → Lafferty 2001 Conditional Random Fields
- Lafferty et al ICML 2001 → Lafferty 2001 Conditional Random Fields
- Lange et al CIKM 2010 → Paper 1
- Learning Influence Probabilities In Social Networks → Goyal et al., 2010, Learning Influence Probabilities In Social Networks
- Learning with Large Datasets - course proposal → Machine Learning with Large Datasets - course proposal
- Lin 2009 phrase clustering for discriminative learning → Lin and Wu. 2009. Phrase Clustering for Discriminative Learning.
- Lin and Wu ACL 2009 → Lin and Wu. 2009. Phrase Clustering for Discriminative Learning.
- Machine Learning with Large Datasets - course proposal → Machine Learning with Large Datasets 10-605
- Mann and McCallum, 2007 → Mann and McCallum, NAACL- 2007
- McDonald et al, ACL 2005: Non-projective dependency parsing using spanning tree algorithms → McDonald et al, ACL 2005: Non-Projective Dependency Parsing Using Spanning Tree Algorithms
- Mkas paper review Jan4 → User talk:Mkas
- NER → Named Entity Recognition
- N min cut → Graph Cut
- Online large-margin training → Margin Infused Relaxed Algorithm
- PCFG → PCFGs
- POS → Part of Speech Tagging
- POS Tagging → Part of Speech Tagging
- POS tagging → POS Tagging
- Paper 1 → Automatically Refining the Wikipedia Infobox Ontology
- Project Adam Gabriel → Forum-Based Language Learning Analysis
- Project Brainstorming for 10-701 in Fall 2011 → Project Brainstorming for 10-710 in Fall 2011
- Project Midterm Status Report, Rushin Kevin Bo → Project Midterm Status Report - Rushin, Kevin, Bo
- Project Project Second Draft Proposal:Daniel and Sherry → Project Proposal Second Draft:Daniel and Sherry
- Review for The Structure of Scientific Collaboration Networks → Newman, PNAS, 2001.
- Ritter et al. EMNLP 2011 → Ritter et al, EMNLP 2011. Named Entity Recognition in Tweets: An Experimental Study
- Ritter et al EMNLP 2011 → Ritter et al, EMNLP 2011. Named Entity Recognition in Tweets: An Experimental Study
- Roadrunner → Crescenzi et al, 2001
- Rodriguez KDD 2010 → Rodriguez et al. KDD 2010
- SPF11:Project Ideas → Project Brainstorming for 10-701 in Fall 2011
- Scope learning → Blei et al, 2002
- Semantic Role Labeling with CRF → Semantic Role Labeling with CRFs
- Semi-supervised Generation of Wikipedia Infoboxes → Improving Knowledge-Based Weakly Supervised Information Extraction
- Sha 2002 Shallow Parsing with Conditional Random Fields → Sha 2003 Shallow Parsing with Conditional Random Fields
- Sha 2003 shallow parsing with conditional random fields → Sha 2003 Shallow Parsing with Conditional Random Fields
- Social Network Attribute → Determining Social Network Attributes
- SoftSupervisedTextClassification → Soft Supervised Text Classification
- Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions → Gimpel and Smith, NAACL 2010
- Stacked sequential learning → Cohen and Carvalho, 2005
- Syllabus for Machine Learning 10-601 → Syllabus for Machine Learning 10-601 in Fall 2013
- Tackstrom and McDonald, ECIR 2010. Discovering fine-grained sentiment with latent variable structured prediction models → Tackstrom and McDonald, ECIR 2011. Discovering fine-grained sentiment with latent variable structured prediction models
- Tom et al. SIASP → Tom Broxton el al., Catching a viral video, J Intell Inf Syst 2011
- UsesDataset:Tencent Weibo → Tencent Weibo
- UsesMethod:Conditional Random Fields → Conditional Random Fields
- Wu and Weld ACM 2008 → Wu and Weld WWW 2008
- Y. Borghol et al. Performance Evaluation 68(2011) → Y. Borghol et al. Performance Evaluation 68 2011