Paper Info
Title
Keco: Kernel-Based Online Co-Agreement Algorithm
Abstract
We propose a kernel-based online semi-supervised algorithm that is applicable for large scale learning tasks. In particular, we use a multi-view learning framework and a co-agreement strategy to take into account unlabelled data and to improve classification performance of the algorithm. Unlike the standard online methods our algorithm is naturally applicable to many real-world situations where data is available in multiple representations. In addition our online algorithm allows learning non-linear relations in the data via kernel functions, that are efficiently embedded into the formulation of the algorithm. We test performance of the algorithm on several large-scale LIBSVM and UCI benchmark datasets and demonstrate improved performance in comparison to standard online learning methods. Last but not least, we make a Python implementation of our algorithm available for download (Available at https://github.com/laurensvdwiel/KeCo).
Year
DOI
Venue
2015
10.1007/978-3-319-24282-8_26
DISCOVERY SCIENCE, DS 2015
Keywords
Field
DocType
Kernel, Non-linear, Online, Large-scale, Semi-supervised, Co-agreement, Multi-view, Classification
Online learning,Kernel (linear algebra),Data mining,Online machine learning,Online algorithm,Computer science,Algorithm,Artificial intelligence,Python (programming language),Machine learning,Kernel (statistics),Distributed computing
Conference
Volume
ISSN
Citations 
9356
0302-9743
0
PageRank 
References 
Authors
0.34
6
3
Name
Order
Citations
PageRank
Laurens Wiel100.34
Tom Heskes21519198.44
Evgeni Levin300.34