Synthetic Multi-label Data Streams Jesse Read, Bernhard Pfahringer, Geoff Holmes University of Waikato, Hamilton, New Zealand Feature Space Introduction

Source URL: jmread.github.io

• Machine learning
• Multi-label classification
• Concept drift
• Slashdot
• Artificial intelligence
• World Wide Web
• Learning

Batch-Incremental vs. Instance-Incremental Learning in Dynamic and Evolving Data Jesse Read1 , Albert Bifet2 , Bernhard Pfahringer2 , Geoff Holmes2 1 Department of Signal Theory and Communications

Source URL: users.ics.aalto.fi

• Statistics
• Machine learning
• Learning
• Artificial intelligence
• Statistical classification
• Data mining
• Concept drift
• Massive Online Analysis
• K-nearest neighbors algorithm
• Support vector machine
• Stochastic gradient descent
• Batch processing

Deep Learning in Partially-labeled Data Streams Jesse Read Aalto University and HIIT Helsinki, Finland Fernando Perez-Cruz

Source URL: users.ics.aalto.fi

• Machine learning
• Learning
• Artificial intelligence
• Artificial neural networks
• Statistical classification
• Data mining
• Deep learning
• K-nearest neighbors algorithm
• Multi-label classification
• Deep belief network
• Restricted Boltzmann machine
• Concept drift

Generating Synthetic Multi-label Data Streams Jesse Read, Bernhard Pfahringer, Geoff Holmes Department of Computer Science The University of Waikato Hamilton, New Zealand {jmr30,bernhard,geoff}@cs.waikato.ac.nz

Source URL: users.ics.aalto.fi

• Machine learning
• Multi-label classification
• Concept drift
• Label
• Statistical classification
• Synthetic data
• Classifier chains
• Pattern recognition

Synthetic Multi-label Data Streams Jesse Read, Bernhard Pfahringer, Geoff Holmes University of Waikato, Hamilton, New Zealand Feature Space Introduction

Source URL: users.ics.aalto.fi

• Machine learning
• Concept drift
• Data mining
• Slashdot
• Multi-label classification

Detecting Concept Drift in Classification Over Streaming Graphs Yibo Yao, Lawrence B. Holder School of Electrical Engineering and Computer Science Washington State University, Pullman, WA 99164 {yibo.yao, holder}@wsu.edu

Source URL: www.mlgworkshop.org

• Data mining
• Graph theory
• Line graph
• Concept drift
• Clique
• Entropy
• Graph

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them ˇ Albert Bifet1 , Jesse Read2 , Indr˙e Zliobait˙ e3 , Bernhard Pfahringer4 , and Geoff Holmes4 1

Source URL: perso.telecom-paristech.fr

• Statistical classification
• Naive Bayes classifier
• Bayes classifier
• Support vector machine
• Classifier
• Multiclass classification
• Concept drift
• Massive Online Analysis
• Binary classification
• Classifier chains
• Nearest neighbour classifiers

A Bayesian Approach to Concept Drift Stephen H. Bach, Marcus A. Maloof Introduction Comparing Conditional Distributions

Source URL: stephenbach.net

• Bayesian statistics
• Statistical models
• Machine learning
• Prior probability
• Bayesian network
• Concept drift
• Probability distribution
• Naive Bayes classifier
• Ensemble learning

JMLR: Workshop and Conference Proceedings–25 2nd Workshop on Applications of Pattern Analysis Streaming Multi-label Classification Jesse Read

Source URL: jmlr.csail.mit.edu

• Machine learning
• Statistical classification
• Data mining
• Classifier chains
• Multi-label classification
• Concept drift
• Boosting
• Massive Online Analysis
• Data stream mining
• Pattern recognition
• Binary classification
• Naive Bayes classifier

Regret Minimization With Concept Drift Koby Crammer∗ The Technion Eyal Even-Dar

Source URL: www.jennwv.com