An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Online Algorithms for Mining Semi-structured Data Stream
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Discretization from data streams: applications to histograms and data mining
Proceedings of the 2006 ACM symposium on Applied computing
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Mining adaptively frequent closed unlabeled rooted trees in data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Handling numeric attributes in hoeffding trees
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
The Journal of Machine Learning Research
Mining frequent closed graphs on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A graph mining approach for detecting unknown malwares
Journal of Visual Languages and Computing
A “learning from models” cognitive fault diagnosis system
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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In many problem settings, for example on graph domains, online learning algorithms on streams of data need to respect strict time constraints dictated by the throughput on which the data arrive. When only a limited amount of memory (budget) is available, a learning algorithm will eventually need to discard some of the information used to represent the current solution, thus negatively affecting its classification performance. More importantly, the overhead due to budget management may significantly increase the computational burden of the learning algorithm. In this paper we present a novel approach inspired by the Passive Aggressive and the Lossy Counting algorithms. Our algorithm uses a fast procedure for deleting the less influential features. Moreover, it is able to estimate the weighted frequency of each feature and use it for prediction.