Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernels and Distances for Structured Data
Machine Learning
Multi-Output Regularized Feature Projection
IEEE Transactions on Knowledge and Data Engineering
Hypergraph spectral learning for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Hypergraph-Based Anomaly Detection of High-Dimensional Co-Occurrences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 6th International Conference on Security of Information and Networks
A clique-based method for mining fuzzy graph patterns in anti-money laundering systems
Proceedings of the 6th International Conference on Security of Information and Networks
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Hyper graph is a data structure that captures many-to-many relations. It comes up in various contexts, one of those being the task of detecting fraudulent users of an on-line system given known associations between the users and types of activities they take part in. In this work we explore three approaches for applying general-purpose machine learning methods to such data. We evaluate the proposed approaches on a real-life dataset of customers and achieve promising results.