Semi-Supervised Clustering II

Sep 09, 2012

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Outline of lecture. Overview of search-based semi-supervised clusteringSimilarity-based semi-supervised clusteringAltered similarity matrix Similarity metric learningAltered Euclidian distanceCombination of search-based and similarity-based semi-supervised clusteringSummary. Overview of sea

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Semi-Supervised Clustering II

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1. Semi-Supervised Clustering II

Semi-supervised Learning

Semi-supervised Learning. Overview. Introduction to SSL Problem SSL Algorithms. Why SSL?. Data labeling is expensive and difficult Labeling is often unreliable Unlabeled examples Easy to obtain in large numbers e.g. webpage classification, bioinformatics, image classification.

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Semi-Supervised Clustering I

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Classification (Supervised Clustering)

Classification (Supervised Clustering). Naomi Altman Nov '06. Objective. Starting from a sample from known groups: 1) Select a set of genes that identify the groups 2) Compute a function of the expression values that can be used to classify a new sample.

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A Probabilistic Framework for Semi-Supervised Clustering

A Probabilistic Framework for Semi-Supervised Clustering. Sugato Basu Mikhail Bilenko Raymond J. Mooney Dept ment of Computer Sciences University of Texas at Austin Presented by Jingting Zeng. Outline. Introduction Background Algorithm Experiments Conclusion.

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Semi-Supervised Learning

Semi-Supervised Learning. Avrim Blum Carnegie Mellon University. [USC CS Distinguished Lecture Series, 2008]. Semi-Supervised Learning. Supervised Learning = learning from labeled data. Dominant paradigm in Machine Learning.

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Semi-Supervised Learning

Semi-Supervised Learning. Maria-Florina Balcan 02/18/2010. 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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Semi-supervised learning

Semi-supervised learning . LING 572 Fei Xia Week 10: 3/10/2011. Outline. Overview of Semi-supervised learning (SSL) Self-training. Additional Reference. Xiaojin Zhu (2006): Semi-supervised learning literature survey.

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Semi-Supervised Learning

Semi-Supervised Learning. Projects Updates (see web page) Presentation on 12/15 9am Final Exam 12/11, in 34xx Final Problem Set No time extension. Consider the problem of Prepositional Phrase Attachment. Buy car with money ; buy car with wheel

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Semi-supervised learning

Semi-supervised learning . LING 572 Fei Xia Week 10: 3/11/2010. Outline. Overview of Semi-supervised learning (SSL) Self-training. Additional Reference. Xiaojin Zhu (2006): Semi-supervised learning literature survey.

595 views • 29 slides

Semi-Supervised Learning

Semi-Supervised Learning. Can we improve the quality of our learning by combining labeled and unlabeled data Usually a lot more unlabeled data available than labeled Assume a set L of labeled data and U of unlabeled data (from the same distribution)

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Supervised Clustering

Pranjal Awasthi , Carnegie Mellon University Reza Bosagh Zadeh, Stanford University. Supervised Clustering. Clustering is usually unsupervised Full supervision renders task meaningless, find middle ground by interacting with teacher Remove subjective ambiguities according to teacher

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Semi-supervised Learning

Semi-supervised Learning. Delip Rao ( delip@jhu.edu ). Outline. Inductive vs. Transductive learning EM & Mixture Models Self-Training Co-Training Manifold regularization (graph based methods) SVM ( transductive & S3VMs) Regularization methods Entropy/Label, etc. Learning Settings.

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Semi-Supervised Clustering

Semi-Supervised Clustering. CS 685: Special Topics in Data Mining Spring 2008 Jinze Liu. Outline of lecture. Overview of clustering and classification What is semi-supervised learning? Semi-supervised clustering Semi-supervised classification Semi-supervised clustering

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Efficient Semi-supervised Spectral Co-clustering with Constraints

Efficient Semi-supervised Spectral Co-clustering with Constraints. Xiaoxiao Shi, Wei Fan, Philip S. Yu. Motivation. Co-clustering with constraints Document-word co-clustering. C. How to use?. Co-clustering. Network. Clustering. Doc 1 (ICNP). Doc 2 (ICDM). Doc 3 (AAAI). C. Doc 4 (KDD).

326 views • 13 slides

Semi-Supervised Learning

Semi-Supervised Learning. D. Zhou, O Bousquet, T. Navin Lan, J. Weston, B. Schokopf Presents: Tal Babaioff. Semi Supervised Learning. Use small number of labeled data to label large amount of cheap unlabeled data. Basic idea: similar examples should be given the same classification.

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Semi-Supervised Clustering

Semi-Supervised Clustering. Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02. Outline. Overview of clustering and classification What is semi-supervised learning? Semi-supervised clustering Semi-supervised classification

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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.

535 views • 35 slides

Semi-supervised Learning

Semi-supervised Learning. Rong Jin. Semi-supervised learning. Label propagation Transductive learning Co-training Active learning. Label Propagation. A toy problem Each node in the graph is an example Two examples are labeled Most examples are unlabeled

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Semi-Supervised Learning

Semi-Supervised Learning. Jing xu. Slides. From: Xiaojin (Jerry) Zhu ---An associate professor in the Department of Computer Sciences at University of Wisconsin-Madison. Outline. What’s semi-supervised learning? Self-training algorithm

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Semi-Supervised Clustering

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Semi-Supervised Learning

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