Semi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity Sandra K¨ ubler Indiana University skuebler @indiana.edu

Source URL: cl.indiana.edu

LNCSAgreement-Based Semi-supervised Learning for Skull Stripping

Source URL: pages.ucsd.edu

Filling the Gap: Semi-Supervised Learning for Opinion Detection Across Domains Abstract We investigate the use of Semi-Supervised Learning (SSL) in opinion detection both in

Source URL: cl.indiana.edu

• Machine learning
• Artificial intelligence
• Learning
• Statistical classification
• Cognition
• Natural language processing
• Semi-supervised learning
• Co-training
• Supervised learning
• Domain adaptation
• Sentiment analysis
• Support vector machine

Zodiac: Organizing Large Deployment of Sensors to Create Reusable Applications for Buildings Bharathan Balaji† , Chetan Verma† , Balakrishnan Narayanaswamy† , Yuvraj Agarwal‡ † †

Source URL: mesl.ucsd.edu

• Machine learning
• Systems science
• Information technology
• Information
• Data management
• Business intelligence
• Knowledge representation
• Records management
• Hierarchical clustering
• Semi-supervised learning
• Building automation
• Control system

Towards Open World Recognition Abhijit Bendale, Terrance Boult University of Colorado at Colorado Springs {abendale,tboult}@vast.uccs.edu Abstract

Source URL: www.cv-foundation.org

• Artificial intelligence
• Machine learning
• Learning
• Statistical classification
• Pattern recognition
• Support vector machine
• Supervised learning
• Artificial neural network
• K-nearest neighbors algorithm
• Deep learning
• Boosting
• Semi-supervised learning

A Visual Approach for Interactive Co-Training Qi Han Weimeng Zhu Florian Heimerl

Source URL: poloclub.gatech.edu

• Statistical classification
• Machine learning
• Artificial intelligence
• Learning
• Statistics
• Co-training
• Semi-supervised learning
• Support vector machine
• K-nearest neighbors algorithm
• Naive Bayes classifier
• Multiclass classification
• Margin classifier

Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning Barzan Mozafari⋆ ⋆ Purna Sarkar† Michael Franklin† Michael Jordan† Samuel Madden‡

Source URL: web.eecs.umich.edu

• Machine learning
• Statistical classification
• Statistics
• Learning
• Data
• Support vector machine
• Bootstrapping
• Active learning
• Semi-supervised learning
• K-nearest neighbors algorithm

Learning Online Smooth Predictors for Realtime Camera Planning using Recurrent Decision Trees ˚ Jianhui Chen ˚

Source URL: www.yisongyue.com

• Machine learning
• Artificial intelligence
• Learning
• Kalman filter
• Decision tree learning
• Conditional random field
• Pattern recognition
• Loss function
• Semi-supervised learning
• Structured prediction
• Prediction
• Artificial neural network

A Conditional Random Field for Multiple-Instance Learning Thomas Deselaers Vittorio Ferrari Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland

Source URL: thomas.deselaers.de

• Machine learning
• Learning
• Artificial intelligence
• Multiple instance learning
• Multiple-instance learning
• Support vector machine
• Conditional random field
• Bag
• Semi-supervised learning
• Statistical classification

Supervised and unsupervised learning in phonetic adaptation Dave F. Kleinschmidt1 , Rajeev Raizada1 , and T. Florian Jaeger1,2,3 1 Department {dkleinschmidt, raizada, fjeager} @ bcs.rochester.edu of Brain and Cognitive S

Source URL: mindmodeling.org

• Machine learning
• Cognition
• Linguistics
• Cognitive science
• Human communication
• Phonetics
• Semantics
• Semi-supervised learning
• Speech perception
• Word-sense disambiguation
• Supervised learning
• Categorization