'Labeled examples' diaporamas de présentation

Techniques For Exploiting Unlabeled Data

Techniques For Exploiting Unlabeled Data. Thesis Defense. September 8,2008. Mugizi Rwebangira. Committee: Avrim Blum, CMU (Co-Chair) John Lafferty, CMU (Co-Chair) William Cohen, CMU Xiaojin (Jerry) Zhu, Wisconsin. Motivation. Supervised Machine Learning:. induction.

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isi/natural-language/people/knight3.html

http://www.isi.edu/natural-language/people/knight3.html. Text Classification. David Kauchak cs458 Fall 2012 adapted from: http://www.stanford.edu/class/cs276/handouts/ lecture10-textcat-naivebayes.ppt http://www.stanford.edu/class/cs276/handouts/lecture11-vector-classify.ppt

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Probabilistic Logics as Declarative Bias for Learners

Probabilistic Logics as Declarative Bias for Learners. William W Cohen Carnegie Mellon University ? Google joint with Lidong Bing , Bhuwan Dhingra , Kathryn Rivard Mazaitis , Haitien Sun, William Yang Wong, Fan Yang, Zhilin Yang. Outline. ProPPR :

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Semi-supervised Structured Prediction Models

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New Theoretical Frameworks for Machine Learning

New Theoretical Frameworks for Machine Learning. Maria-Florina Balcan. Thesis Proposal. 05/15/2007. Thanks to My Committee. Avrim Blum. Manuel Blum. Tom Mitchell. Yishay Mansour. Santosh Vempala. The Goal of the Thesis. New Theoretical Frameworks for Modern Machine Learning Paradigms.

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Building Maximum Entropy Text Classifier Using Semi-supervised Learning

Building Maximum Entropy Text Classifier Using Semi-supervised Learning. Zhang, Xinhua. For PhD Qualifying Exam Term Paper. Road map. Introduction: background and application Semi-supervised learning, especially for text classification (survey) Maximum Entropy Models (survey)

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Web Mining ( ???? )

Partially Supervised Learning ( ??????? ). Web Mining ( ???? ). 1011WM05 TLMXM1A Wed 8,9 (15:10-17:00) U705. Min-Yuh Day ??? Assistant Professor ?????? Dept. of Information Management , Tamkang University ???? ?????? http://mail. tku.edu.tw/myday/ 2012-10-24. ???? ( Syllabus).

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Chapter 5: Semi-Supervised Learning

Chapter 5: Semi-Supervised Learning. Outline. Fully supervised learning (traditional classification) Semi-supervised learning (or classification) LU learning : Learning with a small set of L abeled examples and a large set of U nlabeled examples

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Chapter 5: Partially-Supervised Learning

Chapter 5: Partially-Supervised Learning. Outline. Fully supervised learning (traditional classification) Partially (semi-) supervised learning (or classification) Learning with a small set of labeled examples and a large set of unlabeled examples ( LU learning )

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Detecting Genre Shift

Detecting Genre Shift. Mark Dredze, Tim Oates, Christine Piatko Paper to appear at EMNLP-10. Natural Language Processing and Machine Learning. Extracting findings from scientific papers Genetic epidemiology (development domain) PubMed search produces thousands of papers

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Semi-Supervised Learning With Graphs

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Noise Tolerant Learning

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Rare Category Detection

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Kernels and Margins

Kernels and Margins. Maria Florina Balcan 10/25/2011. O. X. O. X. X. O. X. X. O. O. X. X. X. O. O. X. X. O. Linear Separators. Hypothesis class of linear decision surfaces in R n. Instance space: X=R n. w.

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Semi-Supervised Learning

Semi-Supervised Learning. Maria-Florina Balcan. Supervised Learning: Formalization (PAC). X - instance space S l ={(x i , y i )} - labeled examples drawn i.i.d. from some distr. D over X and labeled by some target concept c * labels 2 {-1,1} - binary classification.

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Incorporating Unlabeled Data in the Learning Process

Incorporating Unlabeled Data in the Learning Process. Lecture 25th. Maria Florina Balcan. Supervised Passive Learning. Data Source. Expert / Oracle. Learning Algorithm. Unlabeled examples. Labeled examples. Algorithm outputs a classifier.

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Incorporating Unlabeled Data in the Learning Process

Incorporating Unlabeled Data in the Learning Process. Lecture 25th. Maria Florina Balcan. Supervised Passive Learning. Data Source. Expert / Oracle. Learning Algorithm. Unlabeled examples. Labeled examples. Algorithm outputs a classifier.

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COMP3503 Semi-Supervised Learning

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Semi-supervised Training of Statistical Parsers

Semi-supervised Training of Statistical Parsers. CMSC 35100 Natural Language Processing January 26, 2006. Roadmap. Motivation: Resource Bottleneck Co-training Co-training with different parsers CFG & LTAG Experiments: Initial seed set size Parse selection Domain porting

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Accessories assignment

Accessories assignment. Students will develop a portfolio for ten accessories. Students will label what type of accessories they are and what is their purpose. All pictures and comments need to be on one page using Word or Publisher. 10 Examples 30 pts Labeled Examples 15 pts

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