'Unlabeled data' presentation slideshows

What is the internet?

968 views • 89 slides

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.

886 views • 46 slides

Artificial Intelligence Lecture No. 30

530 views • 46 slides

Inductive Semi-supervised Learning

Inductive Semi-supervised Learning. Gholamreza Haffari Supervised by: Dr. Anoop Sarkar Simon Fraser University, School of Computing Science. Outline of the talk. Introduction to Semi-Supervised Learning (SSL) Classifier based methods EM

812 views • 46 slides

Lecture 24: Model Adaptation and Semi-Supervised Learning

543 views • 38 slides

Semi-supervised learning and self-training

Semi-supervised learning and self-training. LING 572 Fei Xia 02/14/06. Outline. Overview of Semi-supervised learning (SSL) Self-training (a.k.a. Bootstrapping). Additional Reference. Xiaojin Zhu (2006): Semi-supervised learning literature survey.

1.14k views • 36 slides

600.465 Connecting the dots - I (NLP in Practice)

433 views • 35 slides

KDD Cup 2009

597 views • 35 slides

One M@y He@r The Sh@pe of @ Drum

424 views • 32 slides

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.

594 views • 29 slides

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.

581 views • 29 slides

Active learning

Active learning . Haidong Shi, Nanyi Zeng Nov,12,2008. outline. 1, introduction 2, active learning with different methods 3, Employing EM and Pool-based Active Learning for Text Classification. introduction. 1, what is active learning?

914 views • 28 slides

Overview

Overview. Full Bayesian Learning MAP learning Maximun Likelihood Learning Learning Bayesian Networks Fully observable (complete data) With hidden (unobservable) variables. Announcements. There will be an assignment 4 posted with solutions (no marks given for it)

455 views • 28 slides

Final review

Final review . LING572 Fei Xia Week 10: 03/11/2010. Topics covered. Supervised learning: seven algorithms kNN , NB: training and decoding DT, TBL: training and decoding (with binary features) MaxEnt : training (GIS) and decoding SVM: decoding, tree kernel CRF**

480 views • 27 slides

Analysis of greedy active learning

Analysis of greedy active learning. Sanjoy Dasgupta UC San Diego. Standard learning model. Given m labeled points, want to learn a classifier with misclassification rate < ? , chosen from a hypothesis class H with VC dimension d < 1 .

404 views • 23 slides

Semi-supervised Learning on Partially Labeled Imbalanced Data

Semi-supervised Learning on Partially Labeled Imbalanced Data. Jianjun Xie and Tao Xiong. May 16 , 2010. What Problem We Are Facing. Six data sets extracted from six different domains Domains were removed in the contest They are all binary classification problems

307 views • 18 slides

An Overview on Semi-Supervised Learning Methods

An Overview on Semi-Supervised Learning Methods. Matthias Seeger MPI for Biological Cybernetics Tuebingen, Germany. Overview. The SSL Problem Paradigms for SSL. Examples The Importance of Input-dependent Regularization Note : Citations omitted here (given in my literature review). m. y.

575 views • 15 slides

Algorithms For Data Processing

227 views • 12 slides

The Unreasonable Effectiveness of Data

574 views • 10 slides

Unsupervised Feature Selection for Linked Social Media Data

Unsupervised Feature Selection for Linked Social Media Data. Jiliang Tang, Huan Liu Arizona State University. Motivations. Feature selection is effective in dealing with high-dimensional data.

369 views • 9 slides