Total Variation and Eulers Elastica for Supervised Learning

Dec 28, 2012

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Total Variation and Eulers Elastica for Supervised Learning

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Supervised Hebbian Learning

Supervised Hebbian Learning. Hebb’s Postulate.

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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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Supervised Learning

Supervised Learning. Introduction. Key idea Known target concept (predict certain attribute) Find out how other attributes can be used Algorithms Rudimentary Rules (e.g., 1R) Statistical Modeling (e.g., Na ï ve Bayes) Divide and Conquer: Decision Trees Instance-Based Learning

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Total Variation and Geometric Regularization for Inverse Problems

Total Variation and Geometric Regularization for Inverse Problems. Regularization in Statistics September 7-11, 2003 BIRS, Banff, Canada Tony Chan Department of Mathematics, UCLA. Outline. TV & Geometric Regularization (related concepts) PDE and Functional/Analytic based

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Algorithms for Distributed Supervised and Unsupervised Learning

Algorithms for Distributed Supervised and Unsupervised Learning. Haimonti Dutta The Center for Computational Learning Systems (CCLS) Columbia University, New York. About ME. BCSE from Jadavpur University, Kolkata MS from Temple University, PA

560 views • 38 slides

Supervised learning for text

Supervised learning for text. Organizing knowledge. Systematic knowledge structures Ontologies Dewey decimal system, the Library of Congress catalog, the AMS Mathematics Subject Classification, and the US Patent subject classification Web catalogs Yahoo & Dmoz Problem: Manual maintenance.

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Supervised learning

Supervised learning. Early learning algorithms First order gradient methods Second order gradient methods. Early learning algorithms. Designed for single layer neural networks Generally more limited in their applicability Some of them are Perceptron learning LMS or Widrow- Hoff learning

1.09k views • 48 slides

Supervised Learning

Supervised Learning. The supervisor: Perrin Westrich. Outline. What is supervised learning? What is it used for? Approaches. What is supervised learning?. A machine learning technique Training data Validation. Approaches. Neural networks Decision trees Inductive Logic Programming.

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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.

581 views • 31 slides

Supervised Learning

Supervised Learning. Introduction. Why “Learn”?. Machine learning is programming computers to optimize a performance criterion using example data or past experience. There is no need to “learn” to calculate payroll Learning is used when: Human expertise does not exist (navigating on Mars),

2.12k views • 157 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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Transfer for Supervised Learning Tasks

Transfer for Supervised Learning Tasks. HAITHAM BOU AMMAR MAASTRICHT UNIVERSITY. Motivation. Model. Assumptions: Training and Test are from same distribution Training and Test are in same feature space. Not True in many real-world applications. Training Data. ?. Test Data.

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Supervised and semi-supervised learning for NLP

Supervised and semi-supervised learning for NLP. John Blitzer. 自然语言计算组 http://research.microsoft.com/asia/group/nlc/. Why should I know about machine learning?. This is an NLP summer school. Why should I care about machine learning?

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Supervised Learning

Business School Institute of Business Informatics. Supervised Learning. Uwe Lämmel. www.wi.hs-wismar.de/~laemmel U.laemmel@wi.hs-wismar.de. Neural Networks. Idea Artificial Neuron & Network Supervised Learning Unsupervised Learning Data Mining – other Techniques. Feed-Forward Networks

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Total Variation and Related Methods II

Total Variation and Related Methods II. Variational Methods and their Analysis. We investigate the analysis of variational methods in imaging Most general form:. Variational Methods and their Analysis. Questions: Existence Uniqueness

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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.

809 views • 37 slides

Classification and Supervised Learning

Classification and Supervised Learning. Credits Hand, Mannila and Smyth Cook and Swayne Padhraic Smyth’s notes Shawndra Hill notes. Outline. Supervised Learning Overview Linear Discriminant analysis Tree models Probability based and Bayes models. Classification.

779 views • 57 slides

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

482 views • 23 slides

Supervised Learning

Supervised Learning. Berlin Chen 2005. References: 1. E. Alpaydin, Introduction to Machine Learning , Chapter 2 2. Tom M. Mitchell, Machine Learning , Chapter 7 3. T. Hastie et al., The Elements of Statistical Learning , Chapter 2. Supervised Learning. Learning with a Teacher

452 views • 41 slides

Supervised Learning

Supervised Learning. Berlin Chen Graduate Institute of Computer Science & Information Engineering National Taiwan Normal University. References: 1. E. Alpaydin, Introduction to Machine Learning , Chapter 2 2. Tom M. Mitchell, Machine Learning , Chapter 7

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Supervised Learning

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